ImageTextRecognition_Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Text Recognition "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Reading or Recognising Text from Images is a challenging Task in the field of Computer Vision. This is Mainly because Text in Images exhibit Diversity and Variability. Backgrounds of Images are virtually unpredictable as some images may have dark background, some may have light background and there are ven cases where image fonts and background have same color with a small outline differentiating Text from the Background. There might be patterns of Text appearing in the image with different fonts, orientations, and varying lengths. The Text in Images can also be blurred or distored</p> \n",
    "<p>So is there a way we can overcome these challeges and comeup with a solution which can recognise Text in a Variety of Images with a good accuracy?</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. ML Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given an input Image we need to predict the Text in the Image with a reasonable accuracy >80% (Exact match with the actual Text Labels) and should have a good letter match accuracy. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1. Constraints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Low-Latency\n",
    "2. Predict Text from Images with Variable length Labels/Words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Performance Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. CTC_loss during Training\n",
    "2. Accuracy\n",
    "3. Letter Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Dataset Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Dataset used for this Case Study is **MJ Synth Dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Source:** https://www.robots.ox.ac.uk/~vgg/data/text/#sec-synth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Train Data:** 200000 images from MJ Syth Dataset\n",
    "2. **Validation Data:** 12000 images from MJ Syth Dataset\n",
    "3. **Test Data:** 15000 images from MJ Syth Dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**MJ Synth Dataset**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset consists of 9 million images in Grayscale covering 90k English words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('Synth_Dataset.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Dataset Citation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "@InProceedings{Jaderberg14c,\n",
    "  author       = \"Max Jaderberg and Karen Simonyan and Andrea Vedaldi and Andrew Zisserman\",\n",
    "  title        = \"Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition\",\n",
    "  booktitle    = \"Workshop on Deep Learning, NIPS\",\n",
    "  year         = \"2014\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image as pilImg\n",
    "import os \n",
    "import cv2\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering images with labels of length>=4 and length <=12 for this Case Study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Preparing Train Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Extract_image_names(file_path,number):\n",
    "    \"\"\"\n",
    "    Takes the file path of images annotation txt file with the number of images names to be extracted\n",
    "    and returns the list of file names having label length <=12\n",
    "    \"\"\"\n",
    "    with open(file_path) as f:\n",
    "        file_names=f.readlines()\n",
    "        f.close()\n",
    "        count=0\n",
    "        img_names=[]\n",
    "        for file in file_names:\n",
    "            _,label,_=file.split('_')\n",
    "            if len(label)>=4 and len(label)<=12:\n",
    "                img_names.append(file)\n",
    "                count+=1\n",
    "            if count==number:\n",
    "                break\n",
    "        images_names=['SynthImageDataset'+x.strip() for x in img_names]\n",
    "        return images_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images= Extract_image_names('SynthImageDataset/annotation_train.txt',200000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SynthImageDataset./2425/1/115_Lube_45484.jpg 45484',\n",
       " 'SynthImageDataset./2425/1/114_Spencerian_73323.jpg 73323',\n",
       " 'SynthImageDataset./2425/1/112_CARPENTER_11682.jpg 11682',\n",
       " 'SynthImageDataset./2425/1/110_savannas_67969.jpg 67969',\n",
       " 'SynthImageDataset./2425/1/109_unfix_82473.jpg 82473']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The images names contains '.' after SynthImageDataset name which is an invalid file name so we need to remove '.' in begining without removing '.' which represents extention of images and we need to remove the number at the end as it is just the image serial number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_file_names(file_names):\n",
    "    clean_files=[]\n",
    "    for file in file_names:\n",
    "        main_folder,img_loc,extension=file.split('.')\n",
    "        #Removing the image number at the end\n",
    "        extension,_=extension.split(' ')\n",
    "        img_file=main_folder+img_loc+'.'+extension\n",
    "        clean_files.append(img_file)\n",
    "    return clean_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_cleaned=clean_file_names(train_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SynthImageDataset/2425/1/115_Lube_45484.jpg',\n",
       " 'SynthImageDataset/2425/1/114_Spencerian_73323.jpg',\n",
       " 'SynthImageDataset/2425/1/112_CARPENTER_11682.jpg',\n",
       " 'SynthImageDataset/2425/1/110_savannas_67969.jpg',\n",
       " 'SynthImageDataset/2425/1/109_unfix_82473.jpg']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_cleaned[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=pd.DataFrame({'ImageName':train_cleaned})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SynthImageDataset/2425/1/115_Lube_45484.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SynthImageDataset/2425/1/114_Spencerian_73323.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SynthImageDataset/2425/1/112_CARPENTER_11682.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SynthImageDataset/2425/1/110_savannas_67969.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SynthImageDataset/2425/1/109_unfix_82473.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           ImageName\n",
       "0        SynthImageDataset/2425/1/115_Lube_45484.jpg\n",
       "1  SynthImageDataset/2425/1/114_Spencerian_73323.jpg\n",
       "2   SynthImageDataset/2425/1/112_CARPENTER_11682.jpg\n",
       "3    SynthImageDataset/2425/1/110_savannas_67969.jpg\n",
       "4       SynthImageDataset/2425/1/109_unfix_82473.jpg"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The Ground Truth Values are present in the image file names, so we need to extract it and Store it in Ground Truth Column**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_ground_truth(files):\n",
    "    \"\"\"\n",
    "    Given the file names of images, extracts the Ground Truth Values and returns a list of Ground Truth Labels in All Capitals\n",
    "    \"\"\"\n",
    "    txt_labels=[]\n",
    "    for file in files:\n",
    "        folder,ground_truth,image=file.split('_')\n",
    "        ground_truth=ground_truth.upper()\n",
    "        txt_labels.append(ground_truth)\n",
    "    return txt_labels        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Train_ground_truths=extract_ground_truth(train_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['Labels']=Train_ground_truths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageName</th>\n",
       "      <th>Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SynthImageDataset/2425/1/115_Lube_45484.jpg</td>\n",
       "      <td>LUBE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SynthImageDataset/2425/1/114_Spencerian_73323.jpg</td>\n",
       "      <td>SPENCERIAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SynthImageDataset/2425/1/112_CARPENTER_11682.jpg</td>\n",
       "      <td>CARPENTER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SynthImageDataset/2425/1/110_savannas_67969.jpg</td>\n",
       "      <td>SAVANNAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SynthImageDataset/2425/1/109_unfix_82473.jpg</td>\n",
       "      <td>UNFIX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           ImageName      Labels\n",
       "0        SynthImageDataset/2425/1/115_Lube_45484.jpg        LUBE\n",
       "1  SynthImageDataset/2425/1/114_Spencerian_73323.jpg  SPENCERIAN\n",
       "2   SynthImageDataset/2425/1/112_CARPENTER_11682.jpg   CARPENTER\n",
       "3    SynthImageDataset/2425/1/110_savannas_67969.jpg    SAVANNAS\n",
       "4       SynthImageDataset/2425/1/109_unfix_82473.jpg       UNFIX"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.to_csv('Train_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Preparing Validation Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Validation_images=Extract_image_names('SynthImageDataset/annotation_val.txt',12000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_cleaned=clean_file_names(Validation_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_data=pd.DataFrame({'ImageName':val_cleaned})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SynthImageDataset/2697/6/466_MONIKER_49537.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SynthImageDataset/2697/6/464_FIRESTORM_29099.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SynthImageDataset/2697/6/462_Repurchases_64997...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SynthImageDataset/2697/6/461_PIGTAIL_57575.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SynthImageDataset/2697/6/460_landladies_43270.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           ImageName\n",
       "0     SynthImageDataset/2697/6/466_MONIKER_49537.jpg\n",
       "1   SynthImageDataset/2697/6/464_FIRESTORM_29099.jpg\n",
       "2  SynthImageDataset/2697/6/462_Repurchases_64997...\n",
       "3     SynthImageDataset/2697/6/461_PIGTAIL_57575.jpg\n",
       "4  SynthImageDataset/2697/6/460_landladies_43270.jpg"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "Val_ground_truths=extract_ground_truth(val_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_data['Labels']=Val_ground_truths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageName</th>\n",
       "      <th>Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SynthImageDataset/2697/6/466_MONIKER_49537.jpg</td>\n",
       "      <td>MONIKER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SynthImageDataset/2697/6/464_FIRESTORM_29099.jpg</td>\n",
       "      <td>FIRESTORM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SynthImageDataset/2697/6/462_Repurchases_64997...</td>\n",
       "      <td>REPURCHASES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SynthImageDataset/2697/6/461_PIGTAIL_57575.jpg</td>\n",
       "      <td>PIGTAIL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SynthImageDataset/2697/6/460_landladies_43270.jpg</td>\n",
       "      <td>LANDLADIES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           ImageName       Labels\n",
       "0     SynthImageDataset/2697/6/466_MONIKER_49537.jpg      MONIKER\n",
       "1   SynthImageDataset/2697/6/464_FIRESTORM_29099.jpg    FIRESTORM\n",
       "2  SynthImageDataset/2697/6/462_Repurchases_64997...  REPURCHASES\n",
       "3     SynthImageDataset/2697/6/461_PIGTAIL_57575.jpg      PIGTAIL\n",
       "4  SynthImageDataset/2697/6/460_landladies_43270.jpg   LANDLADIES"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_data.to_csv('Validation_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Preparing Test Data** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images=Extract_image_names('SynthImageDataset/annotation_test.txt',15000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_cleaned=clean_file_names(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=pd.DataFrame({'ImageName':test_cleaned})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SynthImageDataset/3000/7/182_slinking_71711.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SynthImageDataset/3000/7/181_REMODELERS_64541.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SynthImageDataset/3000/7/180_Chronographs_1353...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SynthImageDataset/3000/7/179_Impeaching_38222.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SynthImageDataset/3000/7/177_Loots_45256.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           ImageName\n",
       "0    SynthImageDataset/3000/7/182_slinking_71711.jpg\n",
       "1  SynthImageDataset/3000/7/181_REMODELERS_64541.jpg\n",
       "2  SynthImageDataset/3000/7/180_Chronographs_1353...\n",
       "3  SynthImageDataset/3000/7/179_Impeaching_38222.jpg\n",
       "4       SynthImageDataset/3000/7/177_Loots_45256.jpg"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ground_truths=extract_ground_truth(test_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Labels']=test_ground_truths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageName</th>\n",
       "      <th>Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SynthImageDataset/3000/7/182_slinking_71711.jpg</td>\n",
       "      <td>SLINKING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SynthImageDataset/3000/7/181_REMODELERS_64541.jpg</td>\n",
       "      <td>REMODELERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SynthImageDataset/3000/7/180_Chronographs_1353...</td>\n",
       "      <td>CHRONOGRAPHS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SynthImageDataset/3000/7/179_Impeaching_38222.jpg</td>\n",
       "      <td>IMPEACHING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SynthImageDataset/3000/7/177_Loots_45256.jpg</td>\n",
       "      <td>LOOTS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           ImageName        Labels\n",
       "0    SynthImageDataset/3000/7/182_slinking_71711.jpg      SLINKING\n",
       "1  SynthImageDataset/3000/7/181_REMODELERS_64541.jpg    REMODELERS\n",
       "2  SynthImageDataset/3000/7/180_Chronographs_1353...  CHRONOGRAPHS\n",
       "3  SynthImageDataset/3000/7/179_Impeaching_38222.jpg    IMPEACHING\n",
       "4       SynthImageDataset/3000/7/177_Loots_45256.jpg         LOOTS"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.to_csv('Test_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Image Processing -Converting to Single Channel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to Extract The images present in multiple folder paths in the original Big Data set and store them in a specified Train and Validation Data folder by taking the single channel gray scale images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def img_store_single_channel(destination_folder,files):\n",
    "    \"\"\"\n",
    "    Takes the images in a folder, distination folder path and \n",
    "    converts the image to single channel gray scale,\n",
    "    stores the image in the destination folder and returns image destination list\n",
    "    \"\"\"\n",
    "    start=datetime.now()\n",
    "    destination_list=[]\n",
    "    count=1\n",
    "    for file in files:\n",
    "        #Removing the extra folder structures\n",
    "        _,_,_,Name=file.split('/')\n",
    "        _,img,_=Name.split('_')\n",
    "        destination=destination_folder+str(count)+'_'+img+'.jpg'\n",
    "        cv_img=cv2.imread(file)\n",
    "        #So extracting image from any 1 channel gives a single channel Grayscale image\n",
    "        cv_img_sc=cv_resized[:,:,1]\n",
    "        cv2.imwrite(destination,cv_img_sc)\n",
    "        destination_list.append(destination)\n",
    "        count+=1\n",
    "#         if count%10000==0:\n",
    "#             print(\"Processed Images: \",count)\n",
    "    print('Time Taken for Processing: ',datetime.now() - start)\n",
    "    return destination_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Processing Train Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=pd.read_csv('Train_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(['Unnamed: 0'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_files=train_data['ImageName'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed Images:  10000\n",
      "Processed Images:  20000\n",
      "Processed Images:  30000\n",
      "Processed Images:  40000\n",
      "Processed Images:  50000\n",
      "Processed Images:  60000\n",
      "Processed Images:  70000\n",
      "Processed Images:  80000\n",
      "Processed Images:  90000\n",
      "Processed Images:  100000\n",
      "Processed Images:  110000\n",
      "Processed Images:  120000\n",
      "Processed Images:  130000\n",
      "Processed Images:  140000\n",
      "Processed Images:  150000\n",
      "Processed Images:  160000\n",
      "Processed Images:  170000\n",
      "Processed Images:  180000\n",
      "Processed Images:  190000\n",
      "Processed Images:  200000\n",
      "Time Taken for Processing:  1:37:36.491925\n"
     ]
    }
   ],
   "source": [
    "train_dest=img_store_single_channel('Train_data/',train_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Updating Train Dataframe with new destination file paths\n",
    "train_data['ImageName']=train_dest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageName</th>\n",
       "      <th>Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Train_data/1_Lube.jpg</td>\n",
       "      <td>LUBE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Train_data/2_Spencerian.jpg</td>\n",
       "      <td>SPENCERIAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Train_data/3_CARPENTER.jpg</td>\n",
       "      <td>CARPENTER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Train_data/4_savannas.jpg</td>\n",
       "      <td>SAVANNAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Train_data/5_unfix.jpg</td>\n",
       "      <td>UNFIX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ImageName      Labels\n",
       "0        Train_data/1_Lube.jpg        LUBE\n",
       "1  Train_data/2_Spencerian.jpg  SPENCERIAN\n",
       "2   Train_data/3_CARPENTER.jpg   CARPENTER\n",
       "3    Train_data/4_savannas.jpg    SAVANNAS\n",
       "4       Train_data/5_unfix.jpg       UNFIX"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.to_csv('Train_Final.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Processing Validation Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_data=pd.read_csv('Validation_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_data.drop(['Unnamed: 0'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_files=val_data['ImageName'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed Images:  10000\n",
      "Time Taken for Processing:  0:05:37.001844\n"
     ]
    }
   ],
   "source": [
    "val_dest=img_store_single_channel('Val_data/',val_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Updating Validation Dataframe with new destination file paths\n",
    "val_data['ImageName']=val_dest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageName</th>\n",
       "      <th>Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Val_data/1_MONIKER.jpg</td>\n",
       "      <td>MONIKER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Val_data/2_FIRESTORM.jpg</td>\n",
       "      <td>FIRESTORM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Val_data/3_Repurchases.jpg</td>\n",
       "      <td>REPURCHASES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Val_data/4_PIGTAIL.jpg</td>\n",
       "      <td>PIGTAIL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Val_data/5_landladies.jpg</td>\n",
       "      <td>LANDLADIES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ImageName       Labels\n",
       "0      Val_data/1_MONIKER.jpg      MONIKER\n",
       "1    Val_data/2_FIRESTORM.jpg    FIRESTORM\n",
       "2  Val_data/3_Repurchases.jpg  REPURCHASES\n",
       "3      Val_data/4_PIGTAIL.jpg      PIGTAIL\n",
       "4   Val_data/5_landladies.jpg   LANDLADIES"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the updated Validation Dataframe\n",
    "val_data.to_csv('Validation_Final.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Processing Test Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=pd.read_csv('Test_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.drop(['Unnamed: 0'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_files=test_data['ImageName'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time Taken for Processing:  0:09:11.791386\n"
     ]
    }
   ],
   "source": [
    "test_dest=img_store_single_channel('Test_data/',test_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Updating Test Dataframe with new destination file paths\n",
    "test_data['ImageName']=test_dest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageName</th>\n",
       "      <th>Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Test_data/1_slinking.jpg</td>\n",
       "      <td>SLINKING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Test_data/2_REMODELERS.jpg</td>\n",
       "      <td>REMODELERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Test_data/3_Chronographs.jpg</td>\n",
       "      <td>CHRONOGRAPHS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Test_data/4_Impeaching.jpg</td>\n",
       "      <td>IMPEACHING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Test_data/5_Loots.jpg</td>\n",
       "      <td>LOOTS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ImageName        Labels\n",
       "0      Test_data/1_slinking.jpg      SLINKING\n",
       "1    Test_data/2_REMODELERS.jpg    REMODELERS\n",
       "2  Test_data/3_Chronographs.jpg  CHRONOGRAPHS\n",
       "3    Test_data/4_Impeaching.jpg    IMPEACHING\n",
       "4         Test_data/5_Loots.jpg         LOOTS"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the updated Test Dataframe\n",
    "test_data.to_csv('Test_Final.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1. Image Size Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=pd.read_csv('Train_data.csv')\n",
    "val_data=pd.read_csv('Validation_Data.csv')\n",
    "test_data=pd.read_csv('Test_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Write_Image_Sizes(filenames,storage_file):\n",
    "    \"\"\"\n",
    "    Takes the File names, writes the width and height of images in csv along with file names\n",
    "    \"\"\"\n",
    "    store_file=open(storage_file,'w+')\n",
    "    store_file.write(\"ImageName,Height,Width\")\n",
    "    store_file.write(\"\\n\")\n",
    "    cnt=0\n",
    "    for file in filenames:\n",
    "        cv_img=cv2.imread(file)\n",
    "        #img.shape gives (img_height,img_width,img_channel)\n",
    "        store_file.write(str(file)+\",\"+str(cv_img.shape[0])+\",\"+str(cv_img.shape[1]))\n",
    "        store_file.write(\"\\n\")\n",
    "        cnt+=1\n",
    "        if cnt%10000==0:\n",
    "            print(\"Processed Images: \",cnt)\n",
    "    store_file.close()       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_names=list(train_data['ImageName'].values)\n",
    "val_image_names=list(val_data['ImageName'].values)\n",
    "test_image_names=list(test_data['ImageName'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed Images:  10000\n",
      "Processed Images:  20000\n",
      "Processed Images:  30000\n",
      "Processed Images:  40000\n",
      "Processed Images:  50000\n",
      "Processed Images:  60000\n",
      "Processed Images:  70000\n",
      "Processed Images:  80000\n",
      "Processed Images:  90000\n",
      "Processed Images:  100000\n",
      "Processed Images:  110000\n",
      "Processed Images:  120000\n",
      "Processed Images:  130000\n",
      "Processed Images:  140000\n",
      "Processed Images:  150000\n",
      "Processed Images:  160000\n",
      "Processed Images:  170000\n",
      "Processed Images:  180000\n",
      "Processed Images:  190000\n",
      "Processed Images:  200000\n"
     ]
    }
   ],
   "source": [
    "Write_Image_Sizes(train_image_names,'Train_image_sizes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed Images:  10000\n"
     ]
    }
   ],
   "source": [
    "Write_Image_Sizes(val_image_names,'Validation_image_sizes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed Images:  10000\n"
     ]
    }
   ],
   "source": [
    "Write_Image_Sizes(test_image_names,'Test_image_sizes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_img_size=pd.read_csv('Train_image_sizes.csv')\n",
    "val_img_size=pd.read_csv('Validation_image_sizes.csv')\n",
    "test_img_size=pd.read_csv('Test_image_sizes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.038550</td>\n",
       "      <td>115.668015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.333698</td>\n",
       "      <td>39.696249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>608.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Height          Width\n",
       "count  200000.000000  200000.000000\n",
       "mean       31.038550     115.668015\n",
       "std         0.333698      39.696249\n",
       "min         9.000000       1.000000\n",
       "25%        31.000000      88.000000\n",
       "50%        31.000000     109.000000\n",
       "75%        31.000000     136.000000\n",
       "max        32.000000     608.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_img_size.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12000.000000</td>\n",
       "      <td>12000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.037083</td>\n",
       "      <td>115.644167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.326421</td>\n",
       "      <td>39.476965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>110.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Height         Width\n",
       "count  12000.000000  12000.000000\n",
       "mean      31.037083    115.644167\n",
       "std        0.326421     39.476965\n",
       "min       16.000000     25.000000\n",
       "25%       31.000000     88.000000\n",
       "50%       31.000000    110.000000\n",
       "75%       31.000000    136.000000\n",
       "max       32.000000    440.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_img_size.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15000.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.040600</td>\n",
       "      <td>115.166000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.353497</td>\n",
       "      <td>39.400603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>464.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Height         Width\n",
       "count  15000.000000  15000.000000\n",
       "mean      31.040600    115.166000\n",
       "std        0.353497     39.400603\n",
       "min        6.000000      1.000000\n",
       "25%       31.000000     87.000000\n",
       "50%       31.000000    109.000000\n",
       "75%       31.000000    136.000000\n",
       "max       32.000000    464.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_img_size.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Images in Train Data have a mean height of 31 and mean width of ~116\n",
    "2. Almost 75% of Images have width 136 and height 31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Images Height 90 percentile : 31.0\n",
      "Train Images Height 99 percentile : 32.0\n",
      "Train Images Width 90 percentile : 167.0\n",
      "Train Images Width 99 percentile : 240.0\n",
      "============================================================\n",
      "Validation Images Height 90 percentile : 31.0\n",
      "Validation Images Height 99 percentile : 32.0\n",
      "Validation Images Width 90 percentile : 167.0\n",
      "Validation Images Width 99 percentile : 239.0\n",
      "============================================================\n",
      "Test Images Height 90 percentile : 31.0\n",
      "Test Images Height 99 percentile : 32.0\n",
      "Test Images Width 90 percentile : 167.0\n",
      "Test Images Width 99 percentile : 235.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Images Height 90 percentile :\",np.percentile(train_img_size['Height'].values,90))\n",
    "print(\"Train Images Height 99 percentile :\",np.percentile(train_img_size['Height'].values,99))\n",
    "print(\"Train Images Width 90 percentile :\",np.percentile(train_img_size['Width'].values,90))\n",
    "print(\"Train Images Width 99 percentile :\",np.percentile(train_img_size['Width'].values,99))\n",
    "print(\"=\"*60)\n",
    "print(\"Validation Images Height 90 percentile :\",np.percentile(val_img_size['Height'].values,90))\n",
    "print(\"Validation Images Height 99 percentile :\",np.percentile(val_img_size['Height'].values,99))\n",
    "print(\"Validation Images Width 90 percentile :\",np.percentile(val_img_size['Width'].values,90))\n",
    "print(\"Validation Images Width 99 percentile :\",np.percentile(val_img_size['Width'].values,99))\n",
    "print(\"=\"*60)\n",
    "print(\"Test Images Height 90 percentile :\",np.percentile(test_img_size['Height'].values,90))\n",
    "print(\"Test Images Height 99 percentile :\",np.percentile(test_img_size['Height'].values,99))\n",
    "print(\"Test Images Width 90 percentile :\",np.percentile(test_img_size['Width'].values,90))\n",
    "print(\"Test Images Width 99 percentile :\",np.percentile(test_img_size['Width'].values,99))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "1. There is no much difference in 90 and 99 percentile Values of Image Height\n",
    "2. We need to Further check on Image Width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Images Width 90 percentile : 167.0\n",
      "Train Images Width 91 percentile : 170.0\n",
      "Train Images Width 92 percentile : 174.0\n",
      "Train Images Width 93 percentile : 178.0\n",
      "Train Images Width 94 percentile : 183.0\n",
      "Train Images Width 95 percentile : 189.0\n",
      "Train Images Width 96 percentile : 196.0\n",
      "Train Images Width 97 percentile : 205.0\n",
      "Train Images Width 98 percentile : 218.0\n",
      "Train Images Width 99 percentile : 240.0\n",
      "============================================================\n",
      "Validation Images Width 90 percentile : 167.0\n",
      "Validation Images Width 91 percentile : 170.0\n",
      "Validation Images Width 92 percentile : 174.0\n",
      "Validation Images Width 93 percentile : 179.0\n",
      "Validation Images Width 94 percentile : 184.0\n",
      "Validation Images Width 95 percentile : 190.0\n",
      "Validation Images Width 96 percentile : 197.0\n",
      "Validation Images Width 97 percentile : 206.0\n",
      "Validation Images Width 98 percentile : 217.02000000000044\n",
      "Validation Images Width 99 percentile : 239.0\n",
      "============================================================\n",
      "Test Images Width 90 percentile : 167.0\n",
      "Test Images Width 91 percentile : 171.0\n",
      "Test Images Width 92 percentile : 174.0\n",
      "Test Images Width 93 percentile : 179.0\n",
      "Test Images Width 94 percentile : 184.0\n",
      "Test Images Width 95 percentile : 190.0\n",
      "Test Images Width 96 percentile : 196.0\n",
      "Test Images Width 97 percentile : 204.0\n",
      "Test Images Width 98 percentile : 216.0\n",
      "Test Images Width 99 percentile : 235.0\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(\"Train Images Width \"+str(90+i)+ \" percentile :\",np.percentile(train_img_size['Width'].values,90+i))\n",
    "print(\"=\"*60)\n",
    "for i in range(10):\n",
    "    print(\"Validation Images Width \"+str(90+i)+ \" percentile :\",np.percentile(val_img_size['Width'].values,90+i))\n",
    "print(\"=\"*60)\n",
    "for i in range(10):\n",
    "    print(\"Test Images Width \"+str(90+i)+ \" percentile :\",np.percentile(test_img_size['Width'].values,90+i))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "1. There much Variation in Widths in the 90-99 percentile range\n",
    "2. Almost 95 Percentile of the Images have with of 190\n",
    "3. Compartively fewer number of Images have width > 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cdf_image_heights(label_len):\n",
    "    \"\"\"\n",
    "    Takes a list of image heights as input and Plots CDF of image heights\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(10,6))\n",
    "    count_labels=np.array(label_len)\n",
    "    counts, bin_edges = np.histogram(count_labels, bins=8, \n",
    "                                 density = True)\n",
    "    pdf = counts/(sum(counts))\n",
    "    cdf=np.cumsum(pdf)\n",
    "    plt.plot(bin_edges[1:],cdf)\n",
    "    plt.xlabel('Height of Images',fontsize=10)\n",
    "    plt.ylabel('CDF',fontsize=10)\n",
    "    plt.title('CDF Plot of Image Height',fontsize=12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cdf_image_widths(label_len):\n",
    "    \"\"\"\n",
    "    Takes a list of image widths as input and Plots CDF of image widths\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(10,6))\n",
    "    count_labels=np.array(label_len)\n",
    "    counts, bin_edges = np.histogram(count_labels, bins=8, \n",
    "                                 density = True)\n",
    "    pdf = counts/(sum(counts))\n",
    "    cdf=np.cumsum(pdf)\n",
    "    plt.plot(bin_edges[1:],cdf)\n",
    "    plt.xlabel('Width of Images',fontsize=10)\n",
    "    plt.ylabel('CDF',fontsize=10)\n",
    "    plt.title('CDF Plot of Image Width',fontsize=12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train Data Height and Width CDF Plots**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf_image_heights(train_img_size['Height'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf_image_widths(train_img_size['Width'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "1. Most of the Images have a height of 31\n",
    "2. Almost 95% of the Images have a height of 200 or less"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Validation Data Height and Width CDF Plots**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf_image_heights(val_img_size['Height'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kP7+6Q5997GVddFqxvnTNVCZRBQB0CCEMOAkvV+/TRx5aprcMH6B7b5rB3F4AgA7jLwZwgmJ7ajV33lIV9s3X/XPPVn+mnAAAdAJ/NYATsO9wo+bev1R1jc166PZzNXxg76hLAgCkGUbCgE6qb2rW3/+sQq/XHNIPPzRTpw0fEHVJAIA0xEgY0Anurk8vWKUlG3frW++fplkThkZdEgAgTTESBnTC//zhVT22Yos+9d636JrpJVGXAwBIY4QwoIMefvEN3fvMBt14zmj948UToi4HAJDmCGFABzzzyg59/jcv6+1vKdaXrp7CXGAAgJNGCAPasTq2Tx95eJkmjRig731ghvKYCwwA0AX4awIcR9XuWv3dA0s1uG++fnrr2erHXGAAgC7CXxSgDftqGzV33lLVNzbr4dvP1bABzAUGAOg6hDAghfqmZt35swq9UVOrB287RxOZCwwA0MUIYUAr8bjrX8pX6cVNu/XtG6bpvPFFUZcEAMhAXBMGtPL1P7yqx1du0acvOV1XT2MuMABAOAhhQJKfL9msH/x5g246d7Q+fNH4qMsBAGQwQhgQeHrddn3hNy/rnacP039cxVxgAIBwEcIASatie3XXw8s1ZeQgffcD05kLDAAQOv7SIOtV7a7V381bqqL++frJrWXqm8/9KgCA8PHXBlltb22Dbrn/JTU2ux6582zmAgMAdBtGwpC16hqbdceDFYrtPqwf3VymU4cxFxgAoPswEoaslJgLbKWWvr5H371xus4ZNyTqkgAAWYaRMGSlrz75in67aqs+c+npuvKskVGXAwDIQoQwZJ0HX3hdP/zLRn3ovDG6823MBQYAiAYhDFnlqbXbdc/CNXrXpGG6h7nAAAARIoQha6ys2qt/+sUynVEySN+5cbpycwhgAIDoEMKQFd6oqdVtDyxV8YAC/fiWs5kLDAAQOUIYMt6eQw269f6X1BR3zZt7jooHFERdEgAATFGBzHZkLrC9h/XQ7edqQnH/qEsCAEASI2HIYPG465PzV6pi8x598/ppOnssc4EBAHoOQhgy1n/9bp2eWL1Vn71ski4/c0TU5QAAcBRCGDLSvOc36Ud/3aRbZ43V7ReOi7ocAACOQQhDxvnDmm36j9+u1bsnD9fnr5jMXGAAgB6JEIaMsvyNPfroI8t1ZmmhvnMDc4EBAHouQhgyxuaaQ7r9gQoNG9BbP7mlTH3yc6MuCQCANhHCkBF2H2rQrfcvVdxd8+aeraH9mQsMANCzMU8Y0l7LXGDVew/rF3ecq/HMBQYASAOMhCGtNcdd//zLFVr2xh59+/3TNHMMc4EBANIDIQxp7T8XrdPvXt6mz142SZeewVxgAID0EWoIM7NLzOxVM1tvZnenWD7azJ4xs+VmtsrMLguzHmSWnz63ST95bpPmXjBWt184PupyAADolNBCmJnlSrpX0qWSJku60cwmt1rtc5Lmu/t0STdI+n5Y9SCz/P7lbfrSE2v13inD9bnLWx9WAAD0fGGOhJ0jab27b3T3BkmPSLq61TouaWDwepCkLSHWgwxRuXmPPvbIck0bVahvMxcYACBNhRnCSiRVJb2PBW3J7pH0QTOLSVok6Z9S7cjM7jSzCjOr2LlzZxi1Ik1s2nVItz+wVCMG9daPby5T717MBQYASE9hhrBUwxPe6v2Nkua5e6mkyyT9zMyOqcnd73P3MncvKy4uDqFUpIOag/Wae/9LMjPNm3uOipgLDACQxsIMYTFJo5Lel+rY0423SZovSe7+gqTekoaGWBPSVF1js25/sEJb99XpRzeXaezQflGXBADASQkzhC2VNNHMxplZvhIX3i9stc4bkt4pSWY2SYkQxvlGHKU57vrYI8u1omqvvn3DdM0cMzjqkgAAOGmhhTB3b5J0l6QnJa1T4i7INWb2RTO7Kljtk5LuMLOVkn4h6VZ3b33KElnuy0+s1ZNrtusLV0zWJVNPibocAAC6RKiPLXL3RUpccJ/c9oWk12slXRBmDUhvP3luk+5//nXd9tZxmnvBuKjLAQCgyzBjPnqs363eqi8/sVaXTj1Fn71sUtTlAADQpQhh6JEqN+/Wx365QjNGD9Y33z9NOcwFBgDIMIQw9Dgbdx7U7Q9UqKSwj37EXGAAgAxFCEOPsutgvW69f6lyzDRv7tka0i8/6pIAAAhFqBfmA51xuKFZtz1QoR0H6vSLO87TmCLmAgMAZC5CGHqE5rjro48s16rYXv3wgzM1fTRzgQEAMhunIxE5d9cXH1+jp9Zu1z1XTtF7pjAXGAAg8xHCELkf/3WTHnhhs+64cJxumTU26nIAAOgWhDBE6olVW/WVRet0+Rkj9JlLmQsMAJA9CGGIzNLXd+uf569Q2ZjB+t/rz2IuMABAViGEIRIbdh7UHQ9WqJS5wAAAWYoQhm6380C9br3/JeXlmObNPUeDmQsMAJCFmKIC3aq2oUm3P7BUOw/U65d3nq/RRX2jLgkAgEgwEoZu0xx3ffQXy7W6ep++e+MMnTWqMOqSAACIDCNh6BburnsWrtEf1+3Ql66eondPHh51SQAARIqRMHSL+57dqJ8t2ay/v2i8PnT+2KjLAQAgcoQwhO7xlVv0X797RVecOUKffu/pUZcDAECPQAhDqF7atFufnL9S54wdov+Zw1xgAAC0IIQhNOt3HNAdD1Zo1JA+uu/mmcwFBgBAEkIYQrHjQJ1uvX+peuXmaN7cc1TYl7nAAABIRghDl6ttaNJt8ypUc7BBP721TKOGMBcYAACtEcLQpZqa4/qnh5drzZZ9+t4HpuvMUuYCAwAgFeYJQ5dxd/37wjV6+pUd+vI1U/XOScwFBgBAWxgJQ5f5v79s1EMvvqF/uHiCPnjemKjLAQCgRyOEoUv8ZkW1vvr7V3TVWSP1qfe8JepyAADo8QhhOGlLNtboU+WrdO64Ifr6nDOZCwwAgA4ghOGkvLb9gO58sEKji/rqvg+VqSCPucAAAOgIQhhO2I79ibnACnrlat7cszWob6+oSwIAIG0QwnBCDtU36e8eWKo9tQ366S1nq3Qwc4EBANAZTFGBTmtqjuuuh5dp3dYD+vHNZTqjdFDUJQEAkHYYCUOnffmJdXrm1Z360tVT9fbTh0VdDgAAaYkQhk7ZdbBeP1+yWR84d7Q+cO7oqMsBACBtEcLQKY8tr1ZT3DV31tioSwEAIK0RwtBh7q75FVWaNqpQE4cPiLocAADSGiEMHbYqtk9/235Qc8pKoy4FAIC0RwhDh5VXVqkgL0dXnjUy6lIAAEh7hDB0SF1jsxau2KJLpp6igb2ZlBUAgJNFCEOH/GHtdu2va9KcmaOiLgUAgIxACEOHlFdUqaSwj2ZNKIq6FAAAMgIhDO2q3ntYz63fpetmlionx6IuBwCAjEAIQ7serYzJXZozk7siAQDoKoQwHJe7a8GymM4bP0SjhvCQbgAAugohDMf10qbd2lxTywX5AAB0MUIYjqu8Mqb+BXm69IxToi4FAICMQghDmw7WN2nR6q264swR6pufF3U5AABkFEIY2rRo1VbVNjTzmCIAAEJACEObyiurNL64n2aMHhx1KQAAZBxCGFLatOuQlr6+R7NnlsqMucEAAOhqhDCktKCySjkmXTeDU5EAAISBEIZjNMddv6qs1ttOK9bwgb2jLgcAgIxECMMxnlu/S9v21+n6MuYGAwAgLIQwHGN+RZUK+/bSOycNi7oUAAAyFiEMR9lb26Cn1mzXNdNKVJCXG3U5AABkLEIYjrJw5RY1NMc1m4d1AwAQKkIYjlJeEdOkEQM1tWRQ1KUAAJDRCGE44pVt+7W6ep/mMAoGAEDoCGE4orwipl65pmuml0RdCgAAGY8QBklSQ1Ncv15erXdNGq4h/fKjLgcAgIxHCIMk6U+v7NDuQw08rBsAgG4Saggzs0vM7FUzW29md7exzvVmttbM1pjZw2HWg7YtqKzSsAEFetvE4qhLAQAgK+SFtWMzy5V0r6R3S4pJWmpmC919bdI6EyV9RtIF7r7HzJgdNAI7DtTpmVd36vYLxykvl8FRAAC6Q5h/cc+RtN7dN7p7g6RHJF3dap07JN3r7nskyd13hFgP2vDY8mo1x11zZvKYIgAAukuYIaxEUlXS+1jQluw0SaeZ2fNmtsTMLkm1IzO708wqzKxi586dIZWbndxd5RUxzRhdqFOH9Y+6HAAAskaYIcxStHmr93mSJkq6WNKNkn5sZoXHbOR+n7uXuXtZcTHXLHWlFVV79dqOg5rDw7oBAOhWYYawmKTkv+ylkrakWOc37t7o7pskvapEKEM3Ka+MqXevHF1x5oioSwEAIKuEGcKWSppoZuPMLF/SDZIWtlrnMUlvlyQzG6rE6cmNIdaEJHWNzXp85RZdOnWEBvTuFXU5AABkleOGMDObl/T6ls7s2N2bJN0l6UlJ6yTNd/c1ZvZFM7sqWO1JSTVmtlbSM5I+5e41nfkcnLgn12zTgbomHlMEAEAE2pui4qyk1x+T9EBndu7uiyQtatX2haTXLukTwRe6WXlFTKWD++i88UVRlwIAQNZp73Rk6wvpkSFie2r1/IZdmj2zVDk5qe6hAAAAYWpvJKzUzL6jxJ2OLa+PcPePhlYZQvWrymq5S9fN4FQkAABRaC+EfSrpdUWYhaD7xOOuBcuqNGtCkUYN6Rt1OQAAZKXjhjB379Q1YEgPL27arardh/WJd58WdSkAAGStdqeoMLNbzGyZmR0KvirM7ObuKA7hKK+s0oCCPF0yhbnBAACIynFHwoKw9XEl7l5cpsS1YTMkfd3M5O4Phl8iutKBukb9bvU2XTO9RH3yc6MuBwCArNXeSNg/Snqfuz/j7vvcfa+7/0nSdcEypJknVm3V4cZmzSnjgnwAAKLUXggb6O6vt24M2gaGURDCVV4Z04Tifpo+6phHdAIAgG7UXgg7fILL0ANt2HlQlZv3aE7ZKJkxNxgAAFFqb4qKSWa2KkW7SRofQj0I0YLKmHJzTNdOL4m6FAAAsl5HHls0XFJVq/YxkraEUhFC0Rx3PbospotOK9awgb2jLgcAgKzX3unIb0ra7+6bk78k1QbLkCaefW2ntu+v1/VckA8AQI/QXggb6+7HnI509wpJY0OpCKEor6jSkH75esfpw6MuBQAAqP0QdrzzVn26shCEZ8+hBv1x7Q5dPW2k8vPanZ8XAAB0g/b+Ii81sztaN5rZbZIqwykJXe03K6rV0BzXnJmjoi4FAAAE2rsw/+OSfm1mN+nN0FUmKV/S+8IsDF2nvDKmKSMHavJIpnYDAKCnaO8B3tslzTKzt0uaGjQ/EcyajzSwdst+rdmyX/dcOTnqUgAAQJL2RsIkSe7+jKRnQq4FISivrFJ+bo6unsbcYAAA9CRcpZ3BGpriemx5td49ebgG98uPuhwAAJCEEJbBnl63XXtqGzWbucEAAOhxCGEZrLwypuEDC/S2icVRlwIAAFohhGWoHfvr9OdXd+jaGaXKzeFh3QAA9DSEsAz16PJqxV2aM5NTkQAA9ESEsAzk7iqvqFLZmMEaX9w/6nIAAEAKhLAMtOyNvdqw85DmcEE+AAA9FiEsAy2orFKfXrm6/MyRUZcCAADaQAjLMIcbmvX4yq269IxT1L+gQ3PxAgCACBDCMszv12zVwfomHtYNAEAPRwjLMOUVMY0a0kfnjhsSdSkAAOA4CGEZpGp3rRZvqNGcmaOUw9xgAAD0aISwDLKgMiYz6TrmBgMAoMcjhGWIeNy1oDKmCyYMVUlhn6jLAQAA7SCEZYglG2tUvfcwc4MBAJAmCGEZorwypgG98/TeKadEXQoAAOgAQlgG2F/XqN+9vFVXnTVSvXvlRl0OAADoAEJYBnhi1VbVNcY1p4y5wQAASBeEsAwwv6JKE4f111mlg6IuBQAAdBAhLM2t33FAy9/YqzllpTJjbjAAANIFISzNlVfGlJtjumZ6SdSlAACATiCEpbGm5rgeXVatt7+lWMMG9I66HAAA0AmEsDT27Gs7tfNAPRfkAwCQhghhaay8Iqaifvl6x+nDoi4FAAB0EiEsTe0+1KA/rtuua6aXqFcu/4wAAKQb/nqnqceWV6ux2XlMEQAAaYoQlqbKK2M6o2SQTj9lYNSlAACAE0AIS0MvV+/Tuq37Ga41vN4AABQZSURBVAUDACCNEcLS0ILKmPLzcnTVWSOjLgUAAJwgQliaqW9q1mMrqvWeycNV2Dc/6nIAAMAJIoSlmT+u3aG9tY3MDQYAQJojhKWZ8soqjRjUW289dWjUpQAAgJNACEsj2/bV6dm/7dS1M0qUm8PDugEASGeEsDTy6PKY4i7NnsmpSAAA0h0hLE24uxZUxHTO2CEaN7Rf1OUAAICTRAhLE5Wb92jjrkOazdxgAABkBEJYmiiviKlvfq4uP2NE1KUAAIAuQAhLA7UNTfrtqi267IwR6leQF3U5AACgCxDC0sDvVm/ToYZmzZnJqUgAADIFISwNlFdWaUxRX50zbkjUpQAAgC5CCOvh3qip1ZKNuzVnZqnMmBsMAIBMEWoIM7NLzOxVM1tvZncfZ73ZZuZmVhZmPeloQWWVzKRrZ3AqEgCATBJaCDOzXEn3SrpU0mRJN5rZ5BTrDZD0UUkvhlVLuorHXb9aVq23njpUIwv7RF0OAADoQmGOhJ0jab27b3T3BkmPSLo6xXpfkvQ1SXUh1pKWFm+oUfXewzysGwCADBRmCCuRVJX0Pha0HWFm0yWNcvffhlhH2iqvrNLA3nl6z+ThUZcCAAC6WJghLNVV5H5koVmOpG9K+mS7OzK708wqzKxi586dXVhiz7XvcKN+//I2XT2tRL175UZdDgAA6GJhhrCYpOTzaKWStiS9HyBpqqQ/m9nrks6TtDDVxfnufp+7l7l7WXFxcYgl9xy/XbVF9U1xzeExRQAAZKQwQ9hSSRPNbJyZ5Uu6QdLCloXuvs/dh7r7WHcfK2mJpKvcvSLEmtLG/IqY3jJ8gM4oGRR1KQAAIAShhTB3b5J0l6QnJa2TNN/d15jZF83sqrA+NxO8tv2AVlbt1Zwy5gYDACBThfogQndfJGlRq7YvtLHuxWHWkk7KK2PKyzFdM72k/ZUBAEBaYsb8HqaxOa5Hl1Xr7acP09D+BVGXAwAAQkII62H+8upO7TpYr+uZGwwAgIxGCOthyiurNLR/vi5+S3bcBQoAQLYihPUguw7W6+l1O/S+6SXqlcs/DQAAmYy/9D3IY8ur1RR3HlMEAEAWIIT1EO6uBZUxnVU6SKcNHxB1OQAAIGSEsB7i5er9emXbAc1mFAwAgKxACOshyiurVJCXo6vOGhl1KQAAoBsQwnqAusZm/WbFFr13yika1KdX1OUAAIBuQAjrAZ5au137DjfysG4AALIIIawHKK+MaeSg3po1YWjUpQAAgG5CCIvY1n2H9dfXduq6maXKzeFh3QAAZAtCWMQeXVYtd2n2TE5FAgCQTQhhEXJ3lVdU6dxxQzSmqF/U5QAAgG5ECItQxeY9er2mlhnyAQDIQoSwCM1fWqV++bm67IxToi4FAAB0M0JYRA7VN+mJ1Vt1+Zkj1Dc/L+pyAABANyOERWTR6q2qbWjmVCQAAFmKEBaR8sqYxg3tp7Ixg6MuBQAARIAQFoHNNYf00qbdmj2zVGbMDQYAQDYihEVgQWVMOSZdN4O5wQAAyFaEsG7WHHctqIzpwonFOmVQ76jLAQAAESGEdbPn1+/S1n11PKwbAIAsRwjrZuWVMQ3q00vvmjQ86lIAAECECGHdaF9to55cs03XTBup3r1yoy4HAABEiBDWjRau2qKGpjhzgwEAAEJYd1pQUaXTTxmgKSMHRl0KAACIGCGsm7y67YBWxvZpTtko5gYDAACEsO5SXlGlvBzTNdNGRl0KAADoAQhh3aCxOa7HVlTrnZOGqah/QdTlAACAHoAQ1g2eeWWHdh1s0PVckA8AAAKEsG5QXhlT8YACXXRacdSlAACAHoIQFrKdB+r1zCs7dO30EuXl0t0AACCBVBCyx5ZXqynuPKYIAAAchRAWIndXeWWVpo0q1KnDBkRdDgAA6EEIYSFaFdunv20/yCgYAAA4BiEsROWVVSrIy9GVZzE3GAAAOBohLCR1jc1auGKLLp16igb27hV1OQAAoIchhIXkyTXbtL+uiYd1AwCAlAhhIVlQGVNJYR+dP74o6lIAAEAPRAgLQfXew3pu/S5dN7NUOTk8rBsAAByLEBaCRytjcpfmzOSuSAAAkBohrIu5uxYsi+n88UUaNaRv1OUAAIAeihDWxV7atFuba2qZGwwAABwXIayLza+IqX9Bni6dOiLqUgAAQA9GCOtCB+ubtGj1Vl1x5gj1yc+NuhwAANCDEcK60KJVW3W4sZlTkQAAoF2EsC5UXlml8cX9NGP04KhLAQAAPRwhrIts2nVIS1/fozkzR8mMucEAAMDxEcK6yILKKuWYdO2MkqhLAQAAaYAQ1gWa465fVVbrotOKNXxg76jLAQAAaYAQ1gX++tpObdtfx8O6AQBAhxHCukB5ZUyFfXvpnZOGRV0KAABIE4Swk7S3tkFPrdmua6aVqCCPucEAAEDHEMJO0sKVW9TQHGduMAAA0CmEsJNUXhHT5BEDNWXkoKhLAQAAaYQQdhLWbd2v1dX7GAUDAACdRgg7CeUVMfXKNV09jbnBAABA5xDCTlBDU1yPrajWuyYN15B++VGXAwAA0gwh7AT96ZUd2n2oQdczNxgAADgBoYYwM7vEzF41s/VmdneK5Z8ws7VmtsrMnjazMWHW05UWVFZp2IACXThxaNSlAACANBRaCDOzXEn3SrpU0mRJN5rZ5FarLZdU5u5nSlog6Wth1dOVdhyo0zOv7tS1M0qVl8tgIgAA6LwwE8Q5kta7+0Z3b5D0iKSrk1dw92fcvTZ4u0RSWtxm+Otl1WqOO3dFAgCAExZmCCuRVJX0Pha0teU2Sb9LtcDM7jSzCjOr2LlzZxeW2HnurvLKmGaMLtSE4v6R1gIAANJXmCHMUrR5yhXNPiipTNLXUy139/vcvczdy4qLi7uwxM5bUbVX63cc5GHdAADgpOSFuO+YpOSkUippS+uVzOxdkj4r6SJ3rw+xni5RXhlT7145uuLMEVGXAgAA0liYI2FLJU00s3Fmli/pBkkLk1cws+mSfijpKnffEWItXaKusVmPr9yiy6aO0IDevaIuBwAApLHQQpi7N0m6S9KTktZJmu/ua8zsi2Z2VbDa1yX1l1RuZivMbGEbu+sRnlyzTQfqmjSbC/IBAMBJCvN0pNx9kaRFrdq+kPT6XWF+flebX1Gl0sF9dN64oqhLAQAAaY5JrjootqdWizfUaPbMUuXkpLrnAAAAoOMIYR30q8pquUvXzeBUJAAAOHmEsA6Ix10LllXpglOLNGpI36jLAQAAGYAQ1gEvbtqtqt2HNWcmc4MBAICuQQjrgPLKKg0oyNN7p5wSdSkAACBDEMLacaCuUYtWb9UVZ41Un/zcqMsBAAAZghDWjidWbVVdY5yHdQMAgC5FCGtHeWVME4r7afqowqhLAQAAGYQQdhwbdh5U5eY9ur5slMyYGwwAAHQdQthxLKiMKTfH9L4ZJVGXAgAAMgwhrA1NzXE9uiymi08r1rABvaMuBwAAZBhCWBv++toubd9fzwX5AAAgFISwNpRXVmlIv3y94/ThUZcCAAAyECEshT2HGvTHtTt0zbQS5efRRQAAoOuRMFL4zYpqNTQzNxgAAAgPISyF8sqYppYM1KQRA6MuBQAAZChCWCtrtuzTmi37eVg3AAAIFSGslUP1zZo+ulBXTxsZdSkAACCD5UVdQE9zzrgh+vU/XhB1GQAAIMMxEgYAABABQhgAAEAECGEAAAARIIQBAABEgBAGAAAQAUIYAABABAhhAAAAESCEAQAARIAQBgAAEAFCGAAAQAQIYQAAABEghAEAAESAEAYAABABc/eoa+gUM9spaXPUdXSDoZJ2RV1ED0S/HIs+SY1+SY1+SY1+SY1+Sa0z/TLG3YtTLUi7EJYtzKzC3cuirqOnoV+ORZ+kRr+kRr+kRr+kRr+k1lX9wulIAACACBDCAAAAIkAI67nui7qAHop+ORZ9khr9khr9khr9khr9klqX9AvXhAEAAESAkTAAAIAIEMIiYGY/NbMdZvZyUtsQM3vKzF4Lvg8O2s3MvmNm681slZnNiK7ycLXRL/eYWbWZrQi+Lkta9pmgX141s/dGU3X4zGyUmT1jZuvMbI2ZfSxoz+pj5jj9krXHjJn1NrOXzGxl0Cf/EbSPM7MXg2Pll2aWH7QXBO/XB8vHRll/WI7TL/PMbFPSsTItaM+K36EWZpZrZsvN7LfB+6w+Xlqk6JcuP14IYdGYJ+mSVm13S3ra3SdKejp4L0mXSpoYfN0p6QfdVGMU5unYfpGkb7r7tOBrkSSZ2WRJN0iaEmzzfTPL7bZKu1eTpE+6+yRJ50n6SPDzZ/sx01a/SNl7zNRLeoe7nyVpmqRLzOw8SV9Vok8mStoj6bZg/dsk7XH3UyV9M1gvE7XVL5L0qaRjZUXQli2/Qy0+Jmld0vtsP15atO4XqYuPF0JYBNz9WUm7WzVfLemB4PUDkq5Jan/QE5ZIKjSzEd1Tafdqo1/acrWkR9y93t03SVov6ZzQiouQu29192XB6wNK/EehRFl+zBynX9qS8cdM8G9+MHjbK/hySe+QtCBob32stBxDCyS908ysm8rtNsfpl7Zkxe+QJJlZqaTLJf04eG/K8uNFOrZf2nHCxwshrOcY7u5bpcQfF0nDgvYSSVVJ68V0/D80meiuYIj3py2n3JSl/RIM/0+X9KI4Zo5o1S9SFh8zwSmUFZJ2SHpK0gZJe929KVgl+ec+0ifB8n2Sirq34u7Rul/cveVY+UpwrHzTzAqCtqw4VgLfkvSvkuLB+yJxvEjH9kuLLj1eCGE9X6r/y8imW1p/IGmCEqcQtkr636A96/rFzPpL+pWkj7v7/uOtmqItY/smRb9k9THj7s3uPk1SqRIjfZNSrRZ8z4o+kY7tFzObKukzkk6XdLakIZI+HayeFf1iZldI2uHulcnNKVbNquOljX6RQjheCGE9x/aW4cvg+46gPSZpVNJ6pZK2dHNtkXH37cF/POOSfqQ3Tx9lVb+YWS8lgsZD7v5o0Jz1x0yqfuGYSXD3vZL+rMT1coVmlhcsSv65j/RJsHyQOn5JQFpK6pdLglPa7u71ku5X9h0rF0i6ysxel/SIEqchvyWOl2P6xcx+HsbxQgjrORZKuiV4fYuk3yS13xzcfXGepH0tp6CyQavz6u+T1HLn5EJJNwR364xT4oLIl7q7vu4QXHPxE0nr3P0bSYuy+phpq1+y+Zgxs2IzKwxe95H0LiWulXtG0uxgtdbHSssxNFvSnzwDJ49so19eSfqfGFPiuqfkYyXjf4fc/TPuXuruY5W4aeVP7n6Tsvx4aaNfPhjG8ZLX/iroamb2C0kXSxpqZjFJ/y7pvyXNN7PbJL0haU6w+iJJlylxEXGtpLndXnA3aaNfLg5uA3ZJr0v6e0ly9zVmNl/SWiXukvuIuzdHUXc3uEDShyStDq5pkaR/E8dMW/1yYxYfMyMkPRDc9Zkjab67/9bM1kp6xMy+LGm5EuFVwfefmdl6JUY0boii6G7QVr/8ycyKlTidtELSh4P1s+V3qC2fVnYfL215qKuPF2bMBwAAiACnIwEAACJACAMAAIgAIQwAACAChDAAAIAIEMIAAAAiQAgDELrgER8fT3r/pJn9OOn9/5rZJ8xspJktaGMffzazsuD1vyW1jzWzl1Nt08Hais3sRTNbbmYXtvWZANDVCGEAusNiSbMkycxyJA2VNCVp+SxJz7v7FnefnWL71v6t/VU67J2SXnH36e7+1y7cLwAcFyEMQHd4XkEIUyJ8vSzpgJkNDh6CO0nS8uRRLTPrY2aPBA/L/aWkPkH7f0vqY2YrzOyhYJ+5ZvYjM1tjZn8IZkU/ipmNMbOng/09bWajg0ldvybpsmB/x2yXtP1BM/uqmVWa2R/N7JxgpGyjmV0VrDPWzP5qZsuCryPB08y+H9T3WzNbZGazg2UzzewvwX6fTJqV+6Nmtjao95GT7H8APRAhDEDo3H2LpCYzG61EGHtB0ouSzpdUJmmVuze02uwfJNW6+5mSviJpZrCvuyUddvdpwSNWpMQjiO519ymS9kq6LkUZ35P0YLC/hyR9x91XSPqCpF8G+zt8nB+jn6Q/u/tMSQckfVnSu5V4NNIXg3V2SHq3u8+Q9H5J3wnar5U0VtIZkm4Pfu6WZ19+V9LsYL8/DX5WSbpb0vSg3paZuQFkEB5bBKC7tIyGzZL0DUklwet9SpyubO1tCkKMu68ys1XH2femIFBJUqUSgae185UIQ5L0MyVGwDqjQdLvg9erJdW7e6OZrU76vF6SvheMsDVLOi1of6uk8uCh4tvM7Jmg/S2Spkp6KvE4OuVKannm3ColHpPymKTHOlkrgDRACAPQXVquCztDidORVZI+KWm/EiNAqXT0uWr1Sa+bFZy6bEdnn9nWmPSw4njLZ7p73Mxa/lv6z5K2SzpLiTMNdUG7tbFPk7TG3c9PsexyJYLoVZI+b2ZT3L2pkzUD6ME4HQmguzwv6QpJu9292d13SypUYoTqhRTrPyvpJkkys6mSzkxa1hicyuuMxXrzgcM3SXquk9t3xCBJW4MRrw8pMbKl4LOuC64NG67Eg+ol6VVJxWZ25PSkmU0Jbl4Y5e7PSPpXJfqpfwj1AogQIQxAd1mtxF2RS1q17XP3XSnW/4Gk/sFpyH+V9FLSsvskrUq6ML8jPippbrC/D0n6WGeK76DvS7rFzJYocSryUND+K0kxJUYAf6jE9XD7guvgZkv6qpmtlLRCidHCXEk/D051Lpf0TXffG0K9ACJkb46uAwDCYmb93f2gmRUpESgvcPdtUdcFIDpcEwYA3eO3ZlYoKV/SlwhgABgJAwAAiADXhAEAAESAEAYAABABQhgAAEAECGEAAAARIIQBAABEgBAGAAAQgf8HZifLxWHOxuIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf_image_widths(val_img_size['Width'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "1. Most of the Images have a height of 31\n",
    "2. Almost 95% of the Images have a height of 200 or less"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test Data Height and Width CDF Plots**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf_image_heights(test_img_size['Height'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf_image_widths(test_img_size['Width'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "1. Most of the Images have a height of 31\n",
    "2. Almost 95% of the Images have a height of 200 or less"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion**\n",
    "1. Most of the Images have a Height of 31\n",
    "2. Almost 95 % of the Images have a width of 200 or less\n",
    "3. Based on these observations we can resize the images to 32 (Height) x 170 (Width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2. Label Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pdf_label_lengths(label_len):\n",
    "    \"\"\"\n",
    "    Takes a list of label lengths as input and Plots PDF of lengths of Labels\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(10,6))\n",
    "    count_labels=np.array(label_len)\n",
    "    counts, bin_edges = np.histogram(count_labels, bins=8, \n",
    "                                 density = True)\n",
    "    pdf = counts/(sum(counts))\n",
    "    plt.plot(bin_edges[1:],pdf)\n",
    "    plt.xlabel('Length of Labels',fontsize=10)\n",
    "    plt.ylabel('PDF',fontsize=10)\n",
    "    plt.title('PDF Plot of lengths of Labels',fontsize=12)\n",
    "    #Last Element after sorting contains maximum of lengths in labels\n",
    "    max_len=max(label_len)\n",
    "    plt.show()\n",
    "    print('-'*100)\n",
    "    print('Maximum Length of Label: ',max_len)\n",
    "    print('-'*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cdf_label_lengths(label_len):\n",
    "    \"\"\"\n",
    "    Takes a list of label lengths as input and Plots CDF of lengths of Labels\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(10,6))\n",
    "    count_labels=np.array(label_len)\n",
    "    counts, bin_edges = np.histogram(count_labels, bins=8, \n",
    "                                 density = True)\n",
    "    pdf = counts/(sum(counts))\n",
    "    cdf=np.cumsum(pdf)\n",
    "    plt.plot(bin_edges[1:],cdf)\n",
    "    plt.xlabel('Length of Labels',fontsize=10)\n",
    "    plt.ylabel('CDF',fontsize=10)\n",
    "    plt.title('CDF Plot of lengths of Labels',fontsize=12)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_5_lengths_percentage(label_len,counts,total):\n",
    "    \"\"\"\n",
    "    Takes Top 5 Label lengths, their respective counts , total length of labels and computes their percentages\n",
    "    and plots a bar graph with percentage of top 5 label lengths present in the data\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(10,6))\n",
    "    indices=np.arange(len(label_len))\n",
    "    counts=np.array(counts)\n",
    "    percent=(counts/total)*100\n",
    "    plt.bar(indices,percent)\n",
    "    plt.xlabel('Label Lengths',fontsize=10)\n",
    "    plt.ylabel('Percentages',fontsize=10)\n",
    "    plt.title('Percentages of Top 5 lengths of Labels',fontsize=12)\n",
    "    plt.xticks(indices,label_len)\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/19859282/check-if-a-string-contains-a-number\n",
    "import re\n",
    "def hasDigits(inputText):\n",
    "    \"\"\"\n",
    "    Returns True if the given input text has digits in it otherwise returns False\n",
    "    \"\"\"\n",
    "    return bool(re.search(r'\\d', inputText))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def digit_count(labels,size):\n",
    "    \"\"\"\n",
    "    Takes the list of labels and counts the number of labels with and without digits\n",
    "    and prints its percentage\n",
    "    \"\"\"\n",
    "    present=0\n",
    "    absent=0\n",
    "    for i in labels:\n",
    "        pres=hasDigits(i)\n",
    "        if pres==True:\n",
    "            present+=1\n",
    "        else:\n",
    "            absent+=1\n",
    "    present_percent=(present/size)*100\n",
    "    absent_percent=(absent/size)*100\n",
    "    print('Labels with Digits: ',present_percent,' %')\n",
    "    print('Labels without Digits: ',absent_percent,' %')        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.2.1. Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=pd.read_csv('Train_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.drop(['Unnamed: 0'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_label_len=[len(str(x)) for x in train_data['Labels'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the updated Train Dataframe\n",
    "train_data.to_csv('Train_Final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_label_len_dict=Counter(train_label_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({4: 9160,\n",
       "         10: 23919,\n",
       "         9: 30503,\n",
       "         8: 34304,\n",
       "         5: 16790,\n",
       "         6: 25596,\n",
       "         12: 9877,\n",
       "         7: 33812,\n",
       "         11: 16036,\n",
       "         3: 3})"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing Value counts of Label Lengths \n",
    "train_label_len_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/20944483/python-3-sort-a-dict-by-its-values\n",
    "#Sorting dict by value in decending order and storing keys\n",
    "train_keys = [k for k in sorted(train_label_len_dict, key=train_label_len_dict.get, reverse=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking Top 5 keys with highest values \n",
    "train_top_keys=train_keys[:5]\n",
    "train_top_values=[train_label_len_dict.get(k) for k in train_top_keys]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train Data Label Lengths PDF**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Maximum Length of Label:  12\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "pdf_label_lengths(train_label_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "1. Most of the Labels have lengths of 7,8 and 9 in Train Data\n",
    "2. Labels of length 4, 5 and 6 are fewer in number\n",
    "3. Higher length Labels are more in number compared to lower length labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train Data Label Lengths CDF Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf_label_lengths(train_label_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "1. Almost 60% of the Labels have length 9 or less\n",
    "2. There are 20% of the Labels which have length of 6 or less"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train Data Top 5 Label Lengths Count(%) Bar Graph**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_5_lengths_percentage(train_top_keys,train_top_values,len(train_label_len))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "1. There are almost equal number of labels with length 8 and 7 comprising of 17% each of total number of labels of different lengths in Train Data\n",
    "2. Labels of length 9 comprise 15% of total number of labels of different lengths in Train Data \n",
    "3. Labels of length 6 and 10 are almost equal comprising 12.5% of Total number of labels "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train Data Digit Presence Percentage**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels with Digits:  0.07050000000000001  %\n",
      "Labels without Digits:  99.9295  %\n"
     ]
    }
   ],
   "source": [
    "train_labels=[str(x) for x in train_data['Labels'].values]\n",
    "digit_count(train_labels,len(train_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "1. Almost all of the Labels in the Train Data do not have digits present in them comprising 99.925% of total data\n",
    "2. Very Few labels in Train Data have digits present with a very less percentage of 0.0705% of total data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.2.2. Validation Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_data=pd.read_csv('Validation_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_data.drop(['Unnamed: 0'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_label_len=[len(str(x)) for x in val_data['Labels'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_label_len_dict=Counter(val_label_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({7: 2078,\n",
       "         9: 1817,\n",
       "         11: 942,\n",
       "         10: 1525,\n",
       "         8: 1944,\n",
       "         5: 1002,\n",
       "         12: 619,\n",
       "         6: 1584,\n",
       "         4: 489})"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing Value counts of Label Lengths \n",
    "val_label_len_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/20944483/python-3-sort-a-dict-by-its-values\n",
    "#Sorting dict by value in decending order and storing keys\n",
    "val_keys = [k for k in sorted(val_label_len_dict, key=val_label_len_dict.get, reverse=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking Top 5 keys with highest values \n",
    "val_top_keys=val_keys[:5]\n",
    "val_top_values=[val_label_len_dict.get(k) for k in val_top_keys]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Validation Data Label Lengths PDF**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Maximum Length of Label:  12\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "pdf_label_lengths(val_label_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "1. Labels of Length 8,9 are the highest in number among all other Labels in Validation Data\n",
    "2. Labels of Length 11, 12 are almost equal in number in Validation Data\n",
    "3. Labels of Length 6 or less are very few"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Validation Data Label Lengths CDF**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf_label_lengths(val_label_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "1. Almost 60 % of the Labels have length less than or equal to 9\n",
    "2. There is an almost steep increase in CDF between length 6 to 8, meaning there are more number of labels with length in this range in Validation Data\n",
    "3. There are very few labels with length less 6 comprising less than 20% of total labels in Validation Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Validation Data Top 5 Label Lengths Count(%) Bar Graph**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_5_lengths_percentage(val_top_keys,val_top_values,len(val_label_len))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "1. Highest number of labels in Validation Data are of length 7 comprising almost 17.5% of total validation data labels\n",
    "2. Labels of Length 6 and 10 are almost equal in number comprising almost 12.5% each of total data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Validation Data Digit Presence Percentage**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels with Digits:  0.09166666666666666  %\n",
      "Labels without Digits:  99.90833333333333  %\n"
     ]
    }
   ],
   "source": [
    "val_labels=[str(x) for x in val_data['Labels'].values]\n",
    "digit_count(val_labels,len(val_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "1. Most of Labels found in Vlaidation Data do not have digits present in them with a percentage as high as 99.90% of total data\n",
    "2. On the other hand the percentage of labels with digit is 0.091 % of total data, which is very less "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.2.3. Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data=pd.read_csv('Test_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.drop(['Unnamed: 0'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_label_len=[len(str(x)) for x in test_data['Labels'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_label_len_dict=Counter(test_label_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({8: 2490,\n",
       "         10: 1742,\n",
       "         12: 730,\n",
       "         5: 1317,\n",
       "         9: 2271,\n",
       "         11: 1255,\n",
       "         7: 2495,\n",
       "         6: 1990,\n",
       "         4: 710})"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing Value counts of Label Lengths \n",
    "test_label_len_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/20944483/python-3-sort-a-dict-by-its-values\n",
    "#Sorting dict by value in decending order and storing keys\n",
    "test_keys = [k for k in sorted(test_label_len_dict, key=test_label_len_dict.get, reverse=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking Top 5 keys with highest values \n",
    "test_top_keys=test_keys[:5]\n",
    "test_top_values=[test_label_len_dict.get(k) for k in test_top_keys]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test Data Label Lengths PDF**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------------------------------------------\n",
      "Maximum Length of Label:  12\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "pdf_label_lengths(test_label_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "1. Most of the Labels in Test Data have lengths 8,9 and 10\n",
    "2. Labels with length less than 7 are very less compared to labels of other lengths in Test Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test Data Label Lengths CDF**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cdf_label_lengths(test_label_len)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "1. Almost 60% of the Labels have length less than 9 in Test Data\n",
    "2. Very less number of labels have length less than 6 comprising less than 20% of all Test Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test Data Top 5 Label Lengths Count(%) Bar Graph**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_5_lengths_percentage(test_top_keys,test_top_values,len(test_label_len))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "1. Highest number of Labels in Test Data have lengths 7 and 8 comprising more than 16% each of the Total Test Data\n",
    "2. Top 5 Label lengths in Test Data are between 6 and 10 both inclusive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test Data Digit Presence Percentage**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels with Digits:  0.08666666666666667  %\n",
      "Labels without Digits:  99.91333333333333  %\n"
     ]
    }
   ],
   "source": [
    "test_labels=[str(x) for x in test_data['Labels'].values]\n",
    "digit_count(test_labels,len(test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "1. Almost all of the Labels in the Test Data do not have digits present in them comprising 99.91% of total data\n",
    "2. Very Few labels in Test Data have digits present with a very less percentage of 0.086% of total data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Label Letters Data Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=pd.read_csv('Train_Final.csv')\n",
    "val_data=pd.read_csv('Validation_Final.csv')\n",
    "test_data=pd.read_csv('Test_Final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "letters='ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n",
    "train_labels_combined=' '.join([str(x) for x in train_data['Labels'].values])\n",
    "val_labels_combined=' '.join([str(x) for x in val_data['Labels'].values])\n",
    "test_labels_combined=' '.join([str(x) for x in test_data['Labels'].values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "def letters_list(letter,combined_labels):\n",
    "    \"\"\"\n",
    "    Takes the letters, combined labels text checks for the presence of each letter and returns a list of letters \n",
    "    present in the combined labels\n",
    "    \"\"\"\n",
    "    letter_list=[]\n",
    "    for i in range(len(letter)):\n",
    "        if letter[i] in combined_labels:\n",
    "            letter_list.append(letter[i])\n",
    "    return letter_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_letter_set=set(letters_list(letters,train_labels_combined))\n",
    "val_letter_set=set(letters_list(letters,val_labels_combined))\n",
    "test_letter_set=set(letters_list(letters,test_labels_combined))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique Letters in Train Data:  26\n",
      "Number of unique Letters in Validation Data:  26\n",
      "Number of unique Letters in Test Data:  26\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of unique Letters in Train Data: \",len(train_letter_set))\n",
    "print(\"Number of unique Letters in Validation Data: \",len(val_letter_set))\n",
    "print(\"Number of unique Letters in Test Data: \",len(test_letter_set))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "All Letters in the alphabet are present in each of Train, Validation and Test Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Label Data Analysis Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. All of the Top 5 Label lengths present in Train, Validation and Test Data are same with Top 5 Label lengths being **7,8,9,6,10**\n",
    "2. The Distribution of Data in the Digit Presence in Labels perspective is uniform across Train, Validation and Test Data, each comprising almost **99%** of Labels without digits in them\n",
    "3. Top 2 Label Lengths are **7 , 8** across Train, Validation and Test Data\n",
    "4. These Factors ensure that some properties related to Labels in each of Train, Validation and Test Data are same and uniform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.3. Conclusions from Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Most of the Images around 90 % have height 32 and width less than 170\n",
    "2. Most of the Labels present have lengths 7,8 and 9\n",
    "3. Based on this we can re-size the images to 32 (Height) X 170 (Width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Utility Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "import random\n",
    "from keras import backend as K\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Letters present in the Label Text\n",
    "letters= '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#image height\n",
    "img_h=32\n",
    "#image width\n",
    "img_w=170\n",
    "#image Channels\n",
    "img_c=1\n",
    "# classes for softmax with number of letters +1 for blank space in ctc\n",
    "num_classes=len(letters)+1\n",
    "batch_size=64\n",
    "max_length=15 # considering max length of ground truths labels to be 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_words_labels(word):\n",
    "    \"\"\"\n",
    "    Encodes the Ground Truth Labels to a list of Values like eg.HAT returns [17,10,29]\n",
    "    \"\"\"\n",
    "    label_lst=[]\n",
    "    for char in word:\n",
    "        label_lst.append(letters.find(char)) # keeping 0 for blank and for padding labels\n",
    "    return label_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def words_from_labels(labels):\n",
    "    \"\"\"\n",
    "    converts the list of encoded integer labels to word strings like eg. [12,10,29] returns CAT \n",
    "    \"\"\"\n",
    "    txt=[]\n",
    "    for ele in labels:\n",
    "        if ele == len(letters): # CTC blank space\n",
    "            txt.append(\"\")\n",
    "        else:\n",
    "            #print(letters[ele])\n",
    "            txt.append(letters[ele])\n",
    "    return \"\".join(txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ctc_loss_function(args):\n",
    "    \"\"\"\n",
    "    CTC loss function takes the values passed from the model returns the CTC loss using Keras Backend ctc_batch_cost function\n",
    "    \"\"\"\n",
    "    y_pred, y_true, input_length, label_length = args \n",
    "    # since the first couple outputs of the RNN tend to be garbage we need to discard them, found this from other CRNN approaches\n",
    "    # I Tried by including these outputs but the results turned out to be very bad and got very low accuracies on prediction \n",
    "    y_pred = y_pred[:, 2:, :]\n",
    "    return K.ctc_batch_cost(y_true, y_pred, input_length, label_length)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Data Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the CTC loss fuction computed using Keras Backend ctc_batch_cost function requires 4 inputs, we build a **Data genearator class** and define the parameters for our input image and also process the 4 inputs which needs to be passed to the model for computing CTC loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://github.com/qjadud1994/CRNN-Keras\n",
    "#https://keras.io/examples/image_ocr/\n",
    "\n",
    "class DataGenerator(keras.callbacks.Callback):\n",
    "    def __init__(self, img_dirpath, img_w, img_h,\n",
    "                 batch_size,n,output_labels,max_text_len=15):\n",
    "        self.img_h = img_h                    #Image Height\n",
    "        self.img_w = img_w                    #Image Width\n",
    "        self.batch_size = batch_size          #Batch size of Input\n",
    "        self.max_text_len = max_text_len      #Maximum Text length of Labels\n",
    "        \n",
    "#         self.n =len(self.img_dir)                           #Number of images in img_matrix\n",
    "        self.n=n\n",
    "        self.img_dir = img_dirpath[:self.n]     # images list\n",
    "        self.indexes = list(range(self.n))   #List of indices for each image in img_matrix\n",
    "        self.cur_index = 0                   #Current index which points to image being loaded \n",
    "        self.imgs = np.zeros((self.n, self.img_h, self.img_w))\n",
    "        self.texts =  output_labels[:self.n]                  #List of Ground Truth Label texts\n",
    "\n",
    "   \n",
    "    def build_data(self):\n",
    "        \"\"\"\n",
    "        Build The Image Data\n",
    "        \"\"\"\n",
    "        print(self.n, \" Image Loading start...\")\n",
    "        for i, img_file in enumerate(self.img_dir):\n",
    "            img = cv2.imread(img_file)\n",
    "            img = img[:,:,1]                               #Extracting Single Channel Image\n",
    "            img = cv2.resize(img, (self.img_w, self.img_h))\n",
    "            img = img /255\n",
    "            self.imgs[i, :, :]= img\n",
    "            if i%10000==0:\n",
    "                print(\"Loaded Images: \",i)\n",
    "           \n",
    "        print(\"Number of Texts matches with Total Number of Images :\",len(self.texts) == self.n)\n",
    "        print(self.n, \" Image Loading finish...\")\n",
    "\n",
    "\n",
    "    def next_data(self): \n",
    "        \"\"\"\n",
    "        Returns image and text data pointed by the current index\n",
    "        \"\"\"\n",
    "        self.cur_index += 1\n",
    "        #If current index becomes more than the number of images, make current index 0 \n",
    "        #and shuffle the indices list for random picking of image and text data\n",
    "        if self.cur_index >= self.n:\n",
    "            self.cur_index = 0\n",
    "            random.shuffle(self.indexes)\n",
    "        return self.imgs[self.indexes[self.cur_index]], self.texts[self.indexes[self.cur_index]]\n",
    "\n",
    "    def next_batch(self):\n",
    "        \"\"\"\n",
    "        Creates a batch of images images and text data equal to the batch_size,\n",
    "        computes the parameters needed for CTC and returns the inputs to the Model\n",
    "        \"\"\"\n",
    "        while True:\n",
    "            X_data = np.ones([self.batch_size, self.img_w, self.img_h, 1])  #Single channel Gray Size Scale images for input\n",
    "            #Initilizing with -1 to aid for padding labels of different lengths\n",
    "            Y_data = np.ones([self.batch_size, self.max_text_len])* -1        #Text labels for input\n",
    "           #input_length for CTC which is the number of time-steps of the RNN output\n",
    "            input_length = np.ones((self.batch_size, 1)) * 40\n",
    "            label_length = np.zeros((self.batch_size, 1))                   #label length for CTC\n",
    "            source_str=[]                                                   #List to store Ground Truth Labels\n",
    "            for i in range(self.batch_size):\n",
    "                img, text = self.next_data() #getting the image and text data pointed by current index\n",
    "                                    #taking transpose of image\n",
    "                img=img.T\n",
    "                img = np.expand_dims(img, -1)  #expanding image to have a single channel\n",
    "                X_data[i] = img\n",
    "                label=encode_words_labels(text) # encoding label text to integer list and storing in temp label variable\n",
    "                lbl_len=len(label)\n",
    "                Y_data[i,0:lbl_len] = label #Storing the label till its length and padding others\n",
    "                label_length[i] = len(label)\n",
    "                source_str.append(text) #storing Ground Truth Labels which will be accessed as reference for calculating metrics\n",
    "            \n",
    "        #Preparing the input for the Model\n",
    "            inputs = {\n",
    "                'img_input': X_data,  \n",
    "                'ground_truth_labels': Y_data,  \n",
    "                'input_length': input_length,  \n",
    "                'label_length': label_length,\n",
    "                'source_str': source_str  # used for visualization only\n",
    "            }\n",
    "            #Preparing output for the Model and intializing to zeros\n",
    "            outputs = {'ctc': np.zeros([self.batch_size])}  \n",
    "            yield (inputs, outputs) # Return the Prepared input and output to the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Overview of Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Below is the Representation of Model Overview presented in the Research Paper**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('model_overview.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The architecture consists of three parts:**\n",
    "1. convolutional layers, which extract a feature sequence from the input image; \n",
    "2. recurrentlayers, which predict a label distribution for each frame; \n",
    "3. transcription layer, which translates the per-frame predictions into the final label sequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Source: https://ieeexplore.ieee.org/abstract/document/7801919**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Below is the Model Architecture presented in the Research Paper which is implemented as a part of the Case Study for Scene Text Recognition Problem** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Source: https://ieeexplore.ieee.org/abstract/document/7801919**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('model_architecture.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12. Model Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Input, Conv2D, MaxPool2D, Dense,MaxPooling2D\n",
    "from keras.layers import AveragePooling2D, Flatten, Activation, Bidirectional\n",
    "from keras.layers import BatchNormalization, Dropout\n",
    "from keras.layers import Concatenate, Add, Multiply, Lambda\n",
    "from keras.layers import UpSampling2D, Reshape\n",
    "from keras.layers.merge import add,concatenate\n",
    "from keras.layers import Reshape\n",
    "from keras.models import Model\n",
    "from keras.layers.recurrent import LSTM,GRU\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12.1. Model 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model with Bi-Directional LSTM units and Adam Optimizer**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Model Architecture has 2 Stages:\n",
    "1. **Train Stage:** which takes input image, text labels (encoded as integers), input length (time-steps length), label length and outputs CTC loss\n",
    "2. **Prediction Stage:** which takes input image and outputs a matrix of dimesnions 48x37 where 48 is the number of time-steps of RNN and 37 is the length of letters + 1 character for ctc blank "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Image_text_recogniser_model_1(stage,drop_out_rate=0.35):\n",
    "    \"\"\"\n",
    "    Builds the model by taking in the stage variable which specifes the stage,\n",
    "    if the stage is training: model takes inputs required for computing ctc_batch_cost function\n",
    "    else : model takes input as images which is used for prediction\n",
    "    \"\"\"\n",
    "    \n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        input_shape = (1, img_w, img_h)\n",
    "    else:\n",
    "        input_shape = (img_w, img_h, 1)\n",
    "       \n",
    "    model_input=Input(shape=input_shape,name='img_input',dtype='float32')\n",
    "\n",
    "    # Convolution layer \n",
    "    model = Conv2D(64, (3, 3), padding='same', name='conv1', kernel_initializer='he_normal')(model_input) \n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)\n",
    "    model = MaxPooling2D(pool_size=(2, 2), name='max1')(model) \n",
    "\n",
    "    model = Conv2D(128, (3, 3), padding='same', name='conv2', kernel_initializer='he_normal')(model) \n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)\n",
    "    model = MaxPooling2D(pool_size=(2, 2), name='max2')(model) \n",
    "\n",
    "    model = Conv2D(256, (3, 3), padding='same', name='conv3', kernel_initializer='he_normal')(model) \n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)\n",
    "    model = Conv2D(256, (3, 3), padding='same', name='conv4', kernel_initializer='he_normal')(model)\n",
    "    model=Dropout(drop_out_rate)(model)\n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)\n",
    "    model = MaxPooling2D(pool_size=(1, 2), name='max3')(model)  \n",
    "\n",
    "    model = Conv2D(512, (3, 3), padding='same', name='conv5', kernel_initializer='he_normal')(model) \n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)\n",
    "    model = Conv2D(512, (3, 3), padding='same', name='conv6')(model)\n",
    "    model=Dropout(drop_out_rate)(model)\n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)\n",
    "    model = MaxPooling2D(pool_size=(1, 2), name='max4')(model) \n",
    "\n",
    "    model = Conv2D(512, (2, 2), padding='same', kernel_initializer='he_normal', name='con7')(model)\n",
    "    model=Dropout(0.25)(model)\n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)    \n",
    "\n",
    "    # CNN to RNN\n",
    "    model = Reshape(target_shape=((42, 1024)), name='reshape')(model)  \n",
    "    model = Dense(64, activation='relu', kernel_initializer='he_normal', name='dense1')(model)  \n",
    "\n",
    "    # RNN layer\n",
    "    model=Bidirectional(LSTM(256, return_sequences=True, kernel_initializer='he_normal'), merge_mode='sum')(model)\n",
    "    model=Bidirectional(LSTM(256, return_sequences=True, kernel_initializer='he_normal'), merge_mode='concat')(model)\n",
    "\n",
    "    # transforms RNN output to character activations:\n",
    "    model = Dense(num_classes, kernel_initializer='he_normal',name='dense2')(model) \n",
    "    y_pred = Activation('softmax', name='softmax')(model)\n",
    "\n",
    "    \n",
    "    labels = Input(name='ground_truth_labels', shape=[max_length], dtype='float32') \n",
    "    input_length = Input(name='input_length', shape=[1], dtype='int64') \n",
    "    label_length = Input(name='label_length', shape=[1], dtype='int64') \n",
    "\n",
    "    #CTC loss function\n",
    "    loss_out = Lambda(ctc_loss_function, output_shape=(1,),name='ctc')([y_pred, labels, input_length, label_length]) #(None, 1)\n",
    "\n",
    "    if stage=='train':\n",
    "        return model_input,y_pred,Model(inputs=[model_input, labels, input_length, label_length], outputs=loss_out)\n",
    "    else:\n",
    "        return Model(inputs=[model_input], outputs=y_pred)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\dl_env\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\dl_env\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\dl_env\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4249: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\dl_env\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4229: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    }
   ],
   "source": [
    "model_input,y_pred,img_text_recog=Image_text_recogniser_model_1('train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#used for visualization\n",
    "# it is a keras backend function used to capture the model ouputs so that it can be used for decoding and calculating metrics\n",
    "test_func = K.function([model_input], [y_pred])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "img_input (InputLayer)          (None, 170, 32, 1)   0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 170, 32, 64)  640         img_input[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 170, 32, 64)  256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 170, 32, 64)  0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max1 (MaxPooling2D)             (None, 85, 16, 64)   0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2 (Conv2D)                  (None, 85, 16, 128)  73856       max1[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 85, 16, 128)  512         conv2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 85, 16, 128)  0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max2 (MaxPooling2D)             (None, 42, 8, 128)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3 (Conv2D)                  (None, 42, 8, 256)   295168      max2[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 42, 8, 256)   1024        conv3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 42, 8, 256)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4 (Conv2D)                  (None, 42, 8, 256)   590080      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 42, 8, 256)   0           conv4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 42, 8, 256)   1024        dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 42, 8, 256)   0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max3 (MaxPooling2D)             (None, 42, 4, 256)   0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv5 (Conv2D)                  (None, 42, 4, 512)   1180160     max3[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 42, 4, 512)   2048        conv5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 42, 4, 512)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv6 (Conv2D)                  (None, 42, 4, 512)   2359808     activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 42, 4, 512)   0           conv6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 42, 4, 512)   2048        dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 42, 4, 512)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max4 (MaxPooling2D)             (None, 42, 2, 512)   0           activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "con7 (Conv2D)                   (None, 42, 2, 512)   1049088     max4[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 42, 2, 512)   0           con7[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 42, 2, 512)   2048        dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 42, 2, 512)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 42, 1024)     0           activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense1 (Dense)                  (None, 42, 64)       65600       reshape[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_1 (Bidirectional) (None, 42, 256)      657408      dense1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_2 (Bidirectional) (None, 42, 512)      1050624     bidirectional_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense2 (Dense)                  (None, 42, 37)       18981       bidirectional_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "softmax (Activation)            (None, 42, 37)       0           dense2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "ground_truth_labels (InputLayer (None, 15)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_length (InputLayer)       (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "label_length (InputLayer)       (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "ctc (Lambda)                    (None, 1)            0           softmax[0][0]                    \n",
      "                                                                 ground_truth_labels[0][0]        \n",
      "                                                                 input_length[0][0]               \n",
      "                                                                 label_length[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 7,350,373\n",
      "Trainable params: 7,345,893\n",
      "Non-trainable params: 4,480\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "img_text_recog.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Function Takes the final 40x37 output matrices from the model for the batch from test_func function, and takes the argmax of the matrix across each column (which returns a value between 0 to 36 (ctc_blank) both included). The outputs are then merged for repeated values and gives a list of integers. The Final output integers is then converted to final output string text and stored in a list. The Final List containing all decoded outputs are returened by the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_batch(test_func, word_batch):\n",
    "    \"\"\"\n",
    "    Takes the Batch of Predictions and decodes the Predictions by Best Path Decoding and Returns the Output\n",
    "    \"\"\"\n",
    "    out = test_func([word_batch])[0] #returns the predicted output matrix of the model\n",
    "    ret = []\n",
    "    for j in range(out.shape[0]):\n",
    "        out_best = list(np.argmax(out[j, 2:], 1))\n",
    "        out_best = [k for k, g in itertools.groupby(out_best)]\n",
    "        outstr = words_from_labels(out_best)\n",
    "        ret.append(outstr)\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracies(actual_labels,predicted_labels,is_train):\n",
    "    \"\"\"\n",
    "    Takes a List of Actual Outputs, predicted Outputs and returns their accuracy and letter accuracy across\n",
    "    all the labels in the list\n",
    "    \"\"\"\n",
    "    accuracy=0\n",
    "    letter_acc=0\n",
    "    letter_cnt=0\n",
    "    count=0\n",
    "    for i in range(len(actual_labels)):\n",
    "        predicted_output=predicted_labels[i]\n",
    "        actual_output=actual_labels[i]\n",
    "        count+=1\n",
    "        for j in range(min(len(predicted_output),len(actual_output))):\n",
    "            if predicted_output[j]==actual_output[j]:\n",
    "                letter_acc+=1\n",
    "        letter_cnt+=max(len(predicted_output),len(actual_output))\n",
    "        if actual_output==predicted_output:\n",
    "            accuracy+=1\n",
    "    final_accuracy=np.round((accuracy/len(actual_labels))*100,2)\n",
    "    final_letter_acc=np.round((letter_acc/letter_cnt)*100,2)\n",
    "    return final_accuracy,final_letter_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**CallBacks**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://keras.io/examples/image_ocr/\n",
    "class VizCallback(keras.callbacks.Callback):\n",
    "    \"\"\"\n",
    "    The Custom Callback created for printing the Accuracy and Letter Accuracy Metrics at the End of Each Epoch\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, test_func, text_img_gen,is_train,acc_compute_batches):\n",
    "        self.test_func = test_func\n",
    "        self.text_img_gen = text_img_gen\n",
    "        self.is_train=is_train                #used to indicate whether the callback is called to for Train or Validation Data\n",
    "        self.acc_batches=acc_compute_batches  # Number of Batches for which the metrics are computed typically equal to steps/epoch\n",
    "\n",
    "    def show_accuracy_metrics(self,num_batches):\n",
    "        \"\"\"\n",
    "        Calculates the accuracy and letter accuracy for each batch of inputs, \n",
    "        and prints the avarage accuracy and letter accuracy across all the batches\n",
    "        \"\"\"\n",
    "        accuracy=0\n",
    "        letter_accuracy=0\n",
    "        batches_cnt=num_batches\n",
    "        while batches_cnt>0:\n",
    "            word_batch = next(self.text_img_gen)[0]   #Gets the next batch from the Data generator\n",
    "            decoded_res = decode_batch(self.test_func,word_batch['img_input'])\n",
    "            actual_res=word_batch['source_str']\n",
    "            acc,let_acc=accuracies(actual_res,decoded_res,self.is_train)\n",
    "            accuracy+=acc\n",
    "            letter_accuracy+=let_acc\n",
    "            batches_cnt-=1\n",
    "        accuracy=accuracy/num_batches\n",
    "        letter_accuracy=letter_accuracy/num_batches\n",
    "        if self.is_train:\n",
    "            print(\"Train Average Accuracy of \"+str(num_batches)+\" Batches: \",np.round(accuracy,2),\" %\")\n",
    "            print(\"Train Average Letter Accuracy of \"+str(num_batches)+\" Batches: \",np.round(letter_accuracy,2),\" %\")\n",
    "        else:\n",
    "            print(\"Validation Average Accuracy of \"+str(num_batches)+\" Batches: \",np.round(accuracy,2),\" %\")\n",
    "            print(\"Validation Average Letter Accuracy of \"+str(num_batches)+\" Batches: \",np.round(letter_accuracy,2),\" %\")\n",
    "            \n",
    "        \n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        self.show_accuracy_metrics(self.acc_batches)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext tensorboard.notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import EarlyStopping,ModelCheckpoint\n",
    "early_stop=EarlyStopping(monitor='val_loss',patience=2,restore_best_weights=True)\n",
    "model_chk_pt=ModelCheckpoint('weights.{epoch:02d}-{val_loss:.2f}.hdf5', monitor='val_loss', save_best_only=False,save_weights_only=True,verbose=0, mode='auto', period=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "logdir = os.path.join(\"logs_127\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ERROR: Timed out waiting for TensorBoard to start. It may still be running as pid 19124."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%tensorboard --logdir logs_127"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Labels Loading**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading Train Data Labels\n",
    "Train_labels=[str(x) for x in train_data['Labels'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_paths=[str(x) for x in train_data['ImageName'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_nan_cnt=0\n",
    "train_nan_replaced=False\n",
    "for i in range(len(Train_labels)):\n",
    "    if Train_labels[i]=='nan':\n",
    "        Train_labels[i]='NULL'\n",
    "        train_nan_replaced=True\n",
    "        train_nan_cnt+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Was there any NULL values written as Nan in Train Data: True\n",
      "Train Nan count:  3\n"
     ]
    }
   ],
   "source": [
    "print('Was there any NULL values written as Nan in Train Data:',train_nan_replaced)\n",
    "print('Train Nan count: ',train_nan_cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading Validation Data Labels\n",
    "cv_labels=[str(x) for x in val_data['Labels'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_path=[str(x) for x in val_data['ImageName'].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_nan_cnt=0\n",
    "val_nan_replaced=False\n",
    "for i in range(len(cv_labels)):\n",
    "    if cv_labels[i]=='nan':\n",
    "        cv_labels[i]='NULL'\n",
    "        val_nan_replaced=True\n",
    "        val_nan_cnt+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Was there any NULL values written as Nan : False\n",
      "Validation Nan count:  0\n"
     ]
    }
   ],
   "source": [
    "print('Was there any NULL values written as Nan :',val_nan_replaced)\n",
    "print('Validation Nan count: ',val_nan_cnt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Instatiating Data Generator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gene=DataGenerator(train_paths,img_w, img_h,batch_size,200000,Train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200000  Image Loading start...\n",
      "Loaded Images:  0\n",
      "Loaded Images:  10000\n",
      "Loaded Images:  20000\n",
      "Loaded Images:  30000\n",
      "Loaded Images:  40000\n",
      "Loaded Images:  50000\n",
      "Loaded Images:  60000\n",
      "Loaded Images:  70000\n",
      "Loaded Images:  80000\n",
      "Loaded Images:  90000\n",
      "Loaded Images:  100000\n",
      "Loaded Images:  110000\n",
      "Loaded Images:  120000\n",
      "Loaded Images:  130000\n",
      "Loaded Images:  140000\n",
      "Loaded Images:  150000\n",
      "Loaded Images:  160000\n",
      "Loaded Images:  170000\n",
      "Loaded Images:  180000\n",
      "Loaded Images:  190000\n",
      "Number of Texts matches with Total Number of Images : True\n",
      "200000  Image Loading finish...\n"
     ]
    }
   ],
   "source": [
    "train_gene.build_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_num_batches=int(train_gene.n / batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "viz_cb_train = VizCallback( test_func, train_gene.next_batch(),True,train_num_batches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_gen=DataGenerator(val_path,img_w, img_h,batch_size,12000,cv_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12000  Image Loading start...\n",
      "Loaded Images:  0\n",
      "Loaded Images:  10000\n",
      "Number of Texts matches with Total Number of Images : True\n",
      "12000  Image Loading finish...\n"
     ]
    }
   ],
   "source": [
    "val_gen.build_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_num_batches=int(val_gen.n / batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "viz_cb_val = VizCallback( test_func, val_gen.next_batch(),False,val_num_batches)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Defining Optimizer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import optimizers\n",
    "adam=optimizers.Adam()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Keras functionnal API, we can define, train and use a neural network using the Model class. The loss functions that can be used in a class Model have only 2 arguments, the ground truth y_true and the prediction y_pred given in output of the neural network. At present, there is no CTC loss proposed in a Keras Model and, Keras doesn’t currently support loss functions with extra parameters, which is the case for a CTC loss that requires sequence lengths for batch training. Indeed, as observation and label sequences are of variable length, one has to provide the length of each sequence (observation and label), which allows to not consider padding in the loss computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a Dummy Loss function as in Keras there is no CTC loss implementation which actually takes 4 inputs \n",
    "#The loss function in keras accepts only 2 inputs, so create a dummy loss which is a work around for implementing CTC in Keras\n",
    "#The Actual loss computation happens in ctc_loss_function defined above\n",
    "img_text_recog.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=adam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12.1.1. Train Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\dl_env\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Deprecated in favor of operator or tf.math.divide.\n",
      "Epoch 1/20\n",
      "3125/3125 [==============================] - 1374s 440ms/step - loss: 7.4525 - val_loss: 2.5121\n",
      "Train Average Accuracy of 3125 Batches:  71.41  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  86.54  %\n",
      "Validation Average Accuracy of 187 Batches:  70.58  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  85.85  %\n",
      "Epoch 2/20\n",
      "3125/3125 [==============================] - 1373s 439ms/step - loss: 2.0493 - val_loss: 1.8957\n",
      "Train Average Accuracy of 3125 Batches:  78.35  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  90.04  %\n",
      "Validation Average Accuracy of 187 Batches:  76.81  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  89.03  %\n",
      "Epoch 3/20\n",
      "3125/3125 [==============================] - 1370s 438ms/step - loss: 1.6298 - val_loss: 1.5248\n",
      "Train Average Accuracy of 3125 Batches:  82.74  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  92.66  %\n",
      "Validation Average Accuracy of 187 Batches:  81.07  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  91.76  %\n",
      "Epoch 4/20\n",
      "3125/3125 [==============================] - 1370s 439ms/step - loss: 1.4054 - val_loss: 1.4560\n",
      "Train Average Accuracy of 3125 Batches:  84.15  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  93.4  %\n",
      "Validation Average Accuracy of 187 Batches:  81.89  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  92.28  %\n",
      "Epoch 5/20\n",
      "3125/3125 [==============================] - 1371s 439ms/step - loss: 1.2546 - val_loss: 1.3113\n",
      "Train Average Accuracy of 3125 Batches:  86.03  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  94.21  %\n",
      "Validation Average Accuracy of 187 Batches:  83.34  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  92.86  %\n",
      "Epoch 6/20\n",
      "3125/3125 [==============================] - 1370s 438ms/step - loss: 1.1491 - val_loss: 1.2674\n",
      "Train Average Accuracy of 3125 Batches:  87.29  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  94.9  %\n",
      "Validation Average Accuracy of 187 Batches:  84.17  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  93.49  %\n",
      "Epoch 7/20\n",
      "3125/3125 [==============================] - 1370s 439ms/step - loss: 1.0531 - val_loss: 1.2482\n",
      "Train Average Accuracy of 3125 Batches:  87.61  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  94.88  %\n",
      "Validation Average Accuracy of 187 Batches:  84.32  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  93.23  %\n",
      "Epoch 8/20\n",
      "3125/3125 [==============================] - 1370s 438ms/step - loss: 0.9921 - val_loss: 1.2566\n",
      "Train Average Accuracy of 3125 Batches:  87.96  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  95.21  %\n",
      "Validation Average Accuracy of 187 Batches:  84.72  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  93.51  %\n",
      "Epoch 9/20\n",
      "3125/3125 [==============================] - 1370s 438ms/step - loss: 0.9295 - val_loss: 1.1376\n",
      "Train Average Accuracy of 3125 Batches:  89.66  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  95.92  %\n",
      "Validation Average Accuracy of 187 Batches:  85.53  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  94.04  %\n",
      "Epoch 10/20\n",
      "3125/3125 [==============================] - 1369s 438ms/step - loss: 0.8768 - val_loss: 1.1737\n",
      "Train Average Accuracy of 3125 Batches:  89.6  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  95.6  %\n",
      "Validation Average Accuracy of 187 Batches:  85.65  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  93.75  %\n",
      "Epoch 11/20\n",
      "3125/3125 [==============================] - 1901s 608ms/step - loss: 0.8437 - val_loss: 1.1059\n",
      "Train Average Accuracy of 3125 Batches:  90.68  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  96.1  %\n",
      "Validation Average Accuracy of 187 Batches:  86.21  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  94.04  %\n",
      "Epoch 12/20\n",
      "3125/3125 [==============================] - 1372s 439ms/step - loss: 0.8031 - val_loss: 1.0757\n",
      "Train Average Accuracy of 3125 Batches:  91.57  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  96.6  %\n",
      "Validation Average Accuracy of 187 Batches:  86.78  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  94.48  %\n",
      "Epoch 13/20\n",
      "3125/3125 [==============================] - 1370s 439ms/step - loss: 0.7695 - val_loss: 1.0958\n",
      "Train Average Accuracy of 3125 Batches:  91.42  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  96.54  %\n",
      "Validation Average Accuracy of 187 Batches:  86.28  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  94.33  %\n",
      "Epoch 14/20\n",
      "3125/3125 [==============================] - 1370s 439ms/step - loss: 0.7275 - val_loss: 1.0944\n",
      "Train Average Accuracy of 3125 Batches:  91.15  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  96.26  %\n",
      "Validation Average Accuracy of 187 Batches:  86.42  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  94.16  %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x22cb6e024e0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_text_recog.fit_generator(generator=train_gene.next_batch(),\n",
    "                    steps_per_epoch=int(train_gene.n / batch_size),\n",
    "                    epochs=20,\n",
    "                    callbacks=[viz_cb_train,viz_cb_val,train_gene,val_gen,tensorboard_callback,early_stop,model_chk_pt],\n",
    "                    validation_data=val_gen.next_batch(),\n",
    "                    validation_steps=int(val_gen.n / batch_size))\n",
    "#callbacks=[viz_cb_train,viz_cb_val,train_gene,val_gen,tensorboard_callback,early_stop,model_chk_pt],"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_text_recog.save('Best_Img_recog_LSTM_Adam_model_run_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_text_recog.save('Img_recog_LSTM_Adam_model_run_3.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train CTC Loss Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('LSTM_Model_Train_Loss_Plot.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Validation CTC Loss Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('LSTM_Model_Val_loss_Plot.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12.2. Model 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model with Bi-Directional GRU units and RAdam Optimizer**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since GRU is also RNN with faster training time than LSTM, Creating the same architecture with Bi-directional GRU units and RAdam Optimizer to observe if this model architecture gives better results than the previous model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Model Architecture has 2 Stages:\n",
    "1. **Train Stage:** which takes input image, text labels (encoded as integers), input length (time-steps length), label length and outputs CTC loss\n",
    "2. **Prediction Stage:** which takes input image and outputs a matrix of dimesnions 48x37 where 48 is the number of time-steps of RNN and 37 is the length of letters + 1 character for ctc blank "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Image_text_recogniser_model_2(stage,drop_out_rate=0.35):\n",
    "    \"\"\"\n",
    "    Builds the model by taking in the stage variable which specifes the stage,\n",
    "    if the stage is training: model takes inputs required for computing ctc_batch_cost function\n",
    "    else : model takes input as images which is used for prediction\n",
    "    \"\"\"\n",
    "    \n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        input_shape = (1, img_w, img_h)\n",
    "    else:\n",
    "        input_shape = (img_w, img_h, 1)\n",
    "       \n",
    "    model_input=Input(shape=input_shape,name='img_input',dtype='float32')\n",
    "\n",
    "    # Convolution layer \n",
    "    model = Conv2D(64, (3, 3), padding='same', name='conv1', kernel_initializer='he_normal')(model_input) \n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)\n",
    "    model = MaxPooling2D(pool_size=(2, 2), name='max1')(model) \n",
    "\n",
    "    model = Conv2D(128, (3, 3), padding='same', name='conv2', kernel_initializer='he_normal')(model) \n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)\n",
    "    model = MaxPooling2D(pool_size=(2, 2), name='max2')(model) \n",
    "\n",
    "    model = Conv2D(256, (3, 3), padding='same', name='conv3', kernel_initializer='he_normal')(model) \n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)\n",
    "    model = Conv2D(256, (3, 3), padding='same', name='conv4', kernel_initializer='he_normal')(model)\n",
    "    model=Dropout(drop_out_rate)(model)\n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)\n",
    "    model = MaxPooling2D(pool_size=(1, 2), name='max3')(model)  \n",
    "\n",
    "    model = Conv2D(512, (3, 3), padding='same', name='conv5', kernel_initializer='he_normal')(model) \n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)\n",
    "    model = Conv2D(512, (3, 3), padding='same', name='conv6')(model)\n",
    "    model=Dropout(drop_out_rate)(model)\n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)\n",
    "    model = MaxPooling2D(pool_size=(1, 2), name='max4')(model) \n",
    "\n",
    "    model = Conv2D(512, (2, 2), padding='same', kernel_initializer='he_normal', name='con7')(model)\n",
    "    model=Dropout(0.25)(model)\n",
    "    model = BatchNormalization()(model)\n",
    "    model = Activation('relu')(model)\n",
    "#     print(model.shape)\n",
    "    \n",
    "\n",
    "    # CNN to RNN\n",
    "    model = Reshape(target_shape=((42, 1024)), name='reshape')(model)  \n",
    "    model = Dense(64, activation='relu', kernel_initializer='he_normal', name='dense1')(model)  \n",
    "\n",
    "    # RNN layer\n",
    "    model=Bidirectional(GRU(256, return_sequences=True, kernel_initializer='he_normal'), merge_mode='sum')(model)\n",
    "    model=Bidirectional(GRU(256, return_sequences=True, kernel_initializer='he_normal'), merge_mode='concat')(model)\n",
    "\n",
    "    # transforms RNN output to character activations:\n",
    "    model = Dense(num_classes, kernel_initializer='he_normal',name='dense2')(model) \n",
    "    y_pred = Activation('softmax', name='softmax')(model)\n",
    "\n",
    "    \n",
    "    labels = Input(name='ground_truth_labels', shape=[max_length], dtype='float32') \n",
    "    input_length = Input(name='input_length', shape=[1], dtype='int64') \n",
    "    label_length = Input(name='label_length', shape=[1], dtype='int64') \n",
    "\n",
    "    #CTC loss function\n",
    "    loss_out = Lambda(ctc_loss_function, output_shape=(1,),name='ctc')([y_pred, labels, input_length, label_length]) #(None, 1)\n",
    "\n",
    "    if stage=='train':\n",
    "        return model_input,y_pred,Model(inputs=[model_input, labels, input_length, label_length], outputs=loss_out)\n",
    "    else:\n",
    "        return Model(inputs=[model_input], outputs=y_pred)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\dl_env\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\dl_env\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\dl_env\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4249: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\dl_env\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4229: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n"
     ]
    }
   ],
   "source": [
    "model_input,y_pred,img_text_recog=Image_text_recogniser_model_2('train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    " #used for visualization\n",
    "test_func = K.function([model_input], [y_pred])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "img_input (InputLayer)          (None, 170, 32, 1)   0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 170, 32, 64)  640         img_input[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 170, 32, 64)  256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 170, 32, 64)  0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max1 (MaxPooling2D)             (None, 85, 16, 64)   0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2 (Conv2D)                  (None, 85, 16, 128)  73856       max1[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 85, 16, 128)  512         conv2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 85, 16, 128)  0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max2 (MaxPooling2D)             (None, 42, 8, 128)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3 (Conv2D)                  (None, 42, 8, 256)   295168      max2[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 42, 8, 256)   1024        conv3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 42, 8, 256)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4 (Conv2D)                  (None, 42, 8, 256)   590080      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 42, 8, 256)   0           conv4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 42, 8, 256)   1024        dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 42, 8, 256)   0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max3 (MaxPooling2D)             (None, 42, 4, 256)   0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv5 (Conv2D)                  (None, 42, 4, 512)   1180160     max3[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 42, 4, 512)   2048        conv5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 42, 4, 512)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv6 (Conv2D)                  (None, 42, 4, 512)   2359808     activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 42, 4, 512)   0           conv6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 42, 4, 512)   2048        dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 42, 4, 512)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max4 (MaxPooling2D)             (None, 42, 2, 512)   0           activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "con7 (Conv2D)                   (None, 42, 2, 512)   1049088     max4[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 42, 2, 512)   0           con7[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 42, 2, 512)   2048        dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 42, 2, 512)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 42, 1024)     0           activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense1 (Dense)                  (None, 42, 64)       65600       reshape[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_1 (Bidirectional) (None, 42, 256)      493056      dense1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_2 (Bidirectional) (None, 42, 512)      787968      bidirectional_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense2 (Dense)                  (None, 42, 37)       18981       bidirectional_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "softmax (Activation)            (None, 42, 37)       0           dense2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "ground_truth_labels (InputLayer (None, 15)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_length (InputLayer)       (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "label_length (InputLayer)       (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "ctc (Lambda)                    (None, 1)            0           softmax[0][0]                    \n",
      "                                                                 ground_truth_labels[0][0]        \n",
      "                                                                 input_length[0][0]               \n",
      "                                                                 label_length[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 6,923,365\n",
      "Trainable params: 6,918,885\n",
      "Non-trainable params: 4,480\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "img_text_recog.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "viz_cb_train = VizCallback( test_func, train_gene.next_batch(),True,train_num_batches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "viz_cb_val = VizCallback( test_func, val_gen.next_batch(),False,val_num_batches)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Defining RAdam Optimizer**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While Reading Articles on Medium Blog, I came across an article on a New State of the Art Optimizer, Rectified Adam (RAdam). I wanted to try it out and see the results compared to Adam optimizer. So I tried RAdam optimizer with exact same model architecture as the one used for Adam Optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://pypi.org/project/keras-radam/\n",
    "from keras_radam import RAdam\n",
    "Radam=RAdam()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Keras functionnal API, we can define, train and use a neural network using the Model class. The loss functions that can be used in a class Model have only 2 arguments, the ground truth y_true and the prediction y_pred given in output of the neural network. At present, there is no CTC loss proposed in a Keras Model and, Keras doesn’t currently support loss functions with extra parameters, which is the case for a CTC loss that requires sequence lengths for batch training. Indeed, as observation and label sequences are of variable length, one has to provide the length of each sequence (observation and label), which allows to not consider padding in the loss computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a Dummy Loss function as in Keras there is no CTC loss implementation which actually takes 4 inputs \n",
    "#The loss function in keras accepts only 2 inputs, so create a dummy loss which is a work around for implementing CTC in Keras\n",
    "#The Actual loss computation happens in ctc_loss_function defined above\n",
    "img_text_recog.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=Radam)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12.2.1. Train Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ASUS\\Anaconda3\\envs\\dl_env\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Deprecated in favor of operator or tf.math.divide.\n",
      "Epoch 1/20\n",
      "3125/3125 [==============================] - 1275s 408ms/step - loss: 7.6903 - val_loss: 2.7829\n",
      "Train Average Accuracy of 3125 Batches:  69.43  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  85.94  %\n",
      "Validation Average Accuracy of 187 Batches:  69.02  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  85.77  %\n",
      "Epoch 2/20\n",
      "3125/3125 [==============================] - 1270s 406ms/step - loss: 2.2922 - val_loss: 2.2279\n",
      "Train Average Accuracy of 3125 Batches:  75.64  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  89.35  %\n",
      "Validation Average Accuracy of 187 Batches:  74.62  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  88.53  %\n",
      "Epoch 3/20\n",
      "3125/3125 [==============================] - 1272s 407ms/step - loss: 1.8783 - val_loss: 1.9426\n",
      "Train Average Accuracy of 3125 Batches:  78.41  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  90.87  %\n",
      "Validation Average Accuracy of 187 Batches:  77.21  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  90.12  %\n",
      "Epoch 4/20\n",
      "3125/3125 [==============================] - 1275s 408ms/step - loss: 1.6637 - val_loss: 1.7197\n",
      "Train Average Accuracy of 3125 Batches:  81.69  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  92.29  %\n",
      "Validation Average Accuracy of 187 Batches:  79.8  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  91.18  %\n",
      "Epoch 5/20\n",
      "3125/3125 [==============================] - 1275s 408ms/step - loss: 1.5424 - val_loss: 1.5543\n",
      "Train Average Accuracy of 3125 Batches:  82.96  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  93.07  %\n",
      "Validation Average Accuracy of 187 Batches:  80.91  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  91.97  %\n",
      "Epoch 6/20\n",
      "3125/3125 [==============================] - 1729s 553ms/step - loss: 1.4647 - val_loss: 1.5120\n",
      "Train Average Accuracy of 3125 Batches:  83.61  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  93.28  %\n",
      "Validation Average Accuracy of 187 Batches:  81.96  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  92.14  %\n",
      "Epoch 7/20\n",
      "3125/3125 [==============================] - 1276s 408ms/step - loss: 1.4190 - val_loss: 1.5672\n",
      "Train Average Accuracy of 3125 Batches:  83.72  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  93.12  %\n",
      "Validation Average Accuracy of 187 Batches:  81.53  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  92.1  %\n",
      "Epoch 8/20\n",
      "3125/3125 [==============================] - 1277s 409ms/step - loss: 1.4064 - val_loss: 1.6247\n",
      "Train Average Accuracy of 3125 Batches:  83.13  %\n",
      "Train Average Letter Accuracy of 3125 Batches:  92.75  %\n",
      "Validation Average Accuracy of 187 Batches:  80.72  %\n",
      "Validation Average Letter Accuracy of 187 Batches:  91.38  %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x13fee08e780>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_text_recog.fit_generator(generator=train_gene.next_batch(),\n",
    "                    steps_per_epoch=int(train_gene.n / batch_size),\n",
    "                    epochs=20,\n",
    "                    callbacks=[viz_cb_train,viz_cb_val,train_gene,val_gen,tensorboard_callback,early_stop,model_chk_pt],\n",
    "                    validation_data=val_gen.next_batch(),\n",
    "                    validation_steps=int(val_gen.n / batch_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_text_recog.save('Best_Img_recog_GRU_RAdam_model_run_weights.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model 2 Train CTC Loss Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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hCgA+2D/nhcf9+jR9sH/PC4/79GrFFAFf7YP+eFx/36NFWKKACiiigArK1/VpNNs1jsoxNqFySlrEehYDJZvRVHJP4dSK1ayNX8NWGs3C3Vz9qS5jiaKOS3vZrfCtyQfLdcjIHX0oAd4XvLjUfCel3l7J5txPaxySvtC7mKgk4HArm/DevnVtT8671/U4zJeTxwWjWKR2siq7BUWZofmO1c4EmeD6Gt/wzpFx4f8AD2m6X8svkw7biRrmRyrY/g3AkjPbKgDoO1UpNJ17UZbWz1eSyksrW6W5+2I7edPsbcimLYFTnGSGOcdBnhgdMzKi5dgo9ScVQnni+2SHzU/49j/EPWr7IrrtdQw9CM1nz28IvJAIY8fZifuj1pAVPCk0S+FrENIgIQ5BYf3jWx9oh/56x/8AfQrH8KQQt4WsWaJGJQ5JUf3jWv8AZoP+eMf/AHwKAF+0Q/8APWP/AL6FQW88KSyp5qY3ZHzCpvs0H/PGP/vgVA1tAl6p8mPDrj7gqX3Ljs0TSyW80TxySRsjqVYbhyDXK+G7kWgk0eeVd1jefuyW+9GwYg/z/MV1f2aD/njH/wB8CuV1+CDSPFWnaq6ItrN+5n+XhT2J/wA9qwre441O2j9H/SOvC/vIzod1deq/zV0dX9oh/wCesf8A30KPtEP/AD1j/wC+hTVgtnUMsUTKRkEKMEUv2aD/AJ4x/wDfArpOEX7RD/z1j/76FH2iH/nrH/30KT7NB/zxj/74FH2aD/njH/3wKAF+0Q/89Y/++hVeCeH7ZdfvU+8v8Q/uip/s0H/PGP8A74FV4LaD7ZdfuY/vL/AP7ooAs/aIf+esf/fQo+0Q/wDPWP8A76FJ9mg/54x/98Cj7NB/zxj/AO+BQAv2iH/nrH/30KPtEP8Az1j/AO+hSfZoP+eMf/fAo+zQf88Y/wDvgUAR3VxD9jm/ep/q2/iHpToLiH7PH+9T7o/iHpTLq2g+xzfuY/8AVt/APSnQW0H2eP8Acx/dH8A9KAJPtEP/AD1j/wC+hR9oh/56x/8AfQpPs0H/ADxj/wC+BR9mg/54x/8AfAoAX7RD/wA9Y/8AvoUfaIf+esf/AH0KT7NB/wA8Y/8AvgUfZoP+eMf/AHwKAIJp4ft1t+9To/8AEPQVY+0Q/wDPWP8A76FVpraD7dbfuY+j/wAA9BVj7NB/zxj/AO+BQAv2iH/nrH/30KPtEP8Az1j/AO+hSfZoP+eMf/fAo+zQf88Y/wDvgUAL9oh/56x/99Cj7RD/AM9Y/wDvoUn2aD/njH/3wKPs0H/PGP8A74FAEFnPD+//AHqf65v4hVj7RD/z1j/76FVrO2g/f/uY/wDXN/AKsfZoP+eMf/fAoAX7RD/z1j/76FH2iH/nrH/30KT7NB/zxj/74FH2aD/njH/3wKAF+0Q/89Y/++hVe/nh+wyfvU7fxD1qf7NB/wA8Y/8AvgVXv7aD7DJ+5j7fwD1oAs/aIf8AnrH/AN9Cj7RD/wA9Y/8AvoUn2aD/AJ4x/wDfAo+zQf8APGP/AL4FAC/aIf8AnrH/AN9Cj7RD/wA9Y/8AvoUn2aD/AJ4x/wDfAo+zQf8APGP/AL4FAC/aIf8AnrH/AN9Cq5nh/tMfvU/1J/iH94VP9mg/54x/98Cq5toP7TH7mP8A1J/gH94UAWftEP8Az1j/AO+hR9oh/wCesf8A30KT7NB/zxj/AO+BR9mg/wCeMf8A3wKAF+0Q/wDPWP8A76FFJ9mg/wCeMf8A3wKKAJaKKKACiiigAooooAKpT/8AH5J/17H+dWpY/NTbuZPdDg1nT2mLuQefPxbk/f8AegCPwl/yKlj/ALh/9CNbFYHhW23+F7JvOmXKHhXwB8xrX+x/9PFx/wB/KALFV7vhUkH8DZo+x/8ATxcf9/Kjmsd0LDz5zx/z0pPVFRdpIuVR1W1hvYYre5QSRSS4ZT3+U06C23wIftE/TH+sqK6s/ntv38/+uH/LT2NGjWoXcZXW6MK3ubjwfeLZ37NNpErYguDyYT/db2/z7V1qsrqGQhlYZBByCKp3OlQXlu8Fy80sTjDKz5BrmU+0eE75LW+nnk0iVsQzqxzCfRvb/PtXKm8O7P4Py/4H5HoNLGK8f4nVfzea8+66+p2dFVVtVdAyXMzKwyCJcginfY/+ni4/7+V1nmliq8H/AB+XX+8v/oIo+x/9PFx/38qtBZ/6Zdfv5/vL/wAtP9kUAaNFV/sf/Txcf9/KPsf/AE8XH/fygCxRVf7H/wBPFx/38o+x/wDTxcf9/KAH3X/HnN/1zb+VOg/494/90fyqpdWf+hzf6RP/AKtv+WntTobP/R4/9In+6P8Alp7UAXKKr/Y/+ni4/wC/lH2P/p4uP+/lAFiiq/2P/p4uP+/lH2P/AKeLj/v5QAk3/H/bfR/5CrNZ01n/AKdbfv5+j/8ALT2FWfsf/Txcf9/KALFFV/sf/Txcf9/KPsf/AE8XH/fygCxRVf7H/wBPFx/38o+x/wDTxcf9/KACz/5b/wDXZqsVnWln/r/38/8Arm/5aVZ+x/8ATxcf9/KALFFV/sf/AE8XH/fyj7H/ANPFx/38oAsVXv8A/jxk/D+dH2P/AKeLj/v5Va/s8WMn7+ft/wAtPegDRoqv9j/6eLj/AL+UfY/+ni4/7+UAWKKr/Y/+ni4/7+UfY/8Ap4uP+/lAFiq5/wCQoP8Arif/AEIUfY/+ni4/7+VWNn/xMx+/n/1J/wCWnvQBo0VX+x/9PFx/38o+x/8ATxcf9/KALFFV/sf/AE8XH/fyigCxRRRQAVm6prK6bNDbxWd1f3c4ZktrUJuKrjcxLsqgDI6nuMZrSrF8Qa1ZaasdrqOoSaQl0jbNRzGscTDHyl5AVDEZwCOcHvQBo6ffw6nYRXdtu8uQdHGGUg4KkdiCCD9KzrDxG+pXzxWmi6g1sk7wG+LQCLchKscebvxkEfdqt4Nnih8PW9szoA0swtHY7Xu4wxIlwTklgdxPfOeARXOac2mWN1ZQeGdT1BtXe/Ju9Ou7xpHSNnJlMkG7ZGADkMFHO3BO7lgejVSn/wCPyT/r2P8AOrUvmbP3O0N/tg4rOnF59rky0Gfs5z8p6Z+tICPwl/yKlj/uH/0I1sVgeFhdf8IvZeW0IXYcblOfvH3rWxe/3rf/AL5b/GgCzRVbF7/et/8Avlv8aMXv963/AO+W/wAaAFtfl8yP+61F39+2/wCuw/kargXiXpG6D51/un/Gkuhe77bLQf64fwt6H3qY7WLnvc0aiurWG9tnt7qNZIpBhlbvUeL3+9b/APfLf40Yvf71v/3y3+NNpNWZKbTujmoJ7jwbeLa3rPPo0rYhnPJgJ/hPt/8ArHcV1qOsiK6MGVhkMDkEVSubWe8tnt7lbaSKQYZWRuf1rmY5tQ8GzpbXLrNpUrYjmYFvIJ7H2/z61y3eHdn8H5f8D8j0WljFdfxP/SvTz/P1O0qvB/x+XX+8v/oIpqNdyxq8clsyMMqygkEfnUEAvftl180H3l/hb+6Peus800aKrYvf71v/AN8t/jRi9/vW/wD3y3+NAFmiq2L3+9b/APfLf40Yvf71v/3y3+NAEl1/x5zf9c2/lToP+PeP/dH8qqXQvfsc2Wg/1bfwt6fWnQi9+zx/Nb/dH8Len1oAuUVWxe/3rf8A75b/ABoxe/3rf/vlv8aALNFVsXv963/75b/GjF7/AHrf/vlv8aACb/j/ALb6P/IVZrOmF79utvmg6P8Awt6D3qxi9/vW/wD3y3+NAFmiq2L3+9b/APfLf40Yvf71v/3y3+NAFmiq2L3+9b/98t/jRi9/vW//AHy3+NAC2f8Ay3/67NVis60F7+/w0H+ub+Fv8asYvf71v/3y3+NAFmiq2L3+9b/98t/jRi9/vW//AHy3+NAFmq9//wAeMn4fzpMXv963/wC+W/xqvfi9+wyZaDt/C3r9aANGiq2L3+9b/wDfLf40Yvf71v8A98t/jQBZoqti9/vW/wD3y3+NGL3+9b/98t/jQBZquf8AkKD/AK4n/wBCFJi9/vW//fLf41XIvf7THzQZ8k/wt6/WgDRoqti9/vW//fLf40Yvf71v/wB8t/jQBZoqti9/vW//AHy3+NFAFmiiigAooooAKKKKACqU/wDx+Sf9ex/nVqWQxpuCM5/uoOazp7pjdyH7LOP9HIxhfX60AR+Ev+RUsf8AcP8A6Ea2KwPC1yyeF7JRbTPhD8ygYPzH3rW+1t/z6XH5L/jQBZoqt9rb/n0uPyX/ABo+1t/z6XH5L/jQAtz8skUno2DRd/ftv+uw/kahubhnt2H2S4456L/jUU14zraN9ln/ANaOy/3T71PUt6xRp0VW+1t/z6XH5L/jR9rb/n0uPyX/ABqiCzUVzbQ3ds8FzGskUgwysOCKj+1t/wA+lx+S/wCNH2tv+fS4/Jf8aTSasxptO6OYilufBd4sFyzz6LM2I5TyYCex9v8APqK6W0kSW4uJImDoxQqynII2im3Dpd27wXNhNJFIMMrKuCPzrlLVNZ8N3VwumWsl7pysCYZB+8QEZ4xn/PauX3sO7bx/L/gfkei+TGK7aVT7lL/J/mdxRWFpvi6w1N/KiSVLjoYX2hs+gyea1Ptbf8+lx+S/410QnGavF3OGpSnSly1FZlmiq32tv+fS4/Jf8aPtbf8APpcfkv8AjVmZJdf8ec3/AFzb+VOg/wCPeP8A3R/Kql1dt9jm/wBEuP8AVt2X0+tOhu2+zx/6JcfdHZfT60AXKKrfa2/59Lj8l/xo+1t/z6XH5L/jQBZoqt9rb/n0uPyX/Gj7W3/Ppcfkv+NABN/x/wBt9H/kKs1nTXTfbrb/AEWfo/ZfQe9WPtbf8+lx+S/40AWaKrfa2/59Lj8l/wAaPtbf8+lx+S/40AWaKrfa2/59Lj8l/wAaPtbf8+lx+S/40ALZ/wDLf/rs1WKzrS6b9/8A6LP/AK5uy/41Y+1t/wA+lx+S/wCNAFmiq32tv+fS4/Jf8aPtbf8APpcfkv8AjQBZqvf/APHjJ+H86T7W3/Ppcfkv+NV7+6Y2Mn+iz9uy+v1oA0aKrfa2/wCfS4/Jf8aPtbf8+lx+S/40AWaKrfa2/wCfS4/Jf8aPtbf8+lx+S/40AWarn/kKD/rif/QhSfa2/wCfS4/Jf8arm6b+0x/os/8AqT2X1HvQBo0VW+1t/wA+lx+S/wCNH2tv+fS4/Jf8aALNFVvtbf8APpcfkv8AjRQBZooooAKKKxtesIbjF3qeqz2emWsTvMkNw9tk8fO0qMrYAB+XOOcnoKANmisbwlNd3Hhq3lvzMzOXMTTjEjRbj5ZfPO4ptznn15rmtCuTHqKXniGz8QWktxfypBcXN9ItsSZGEaeSJuAVwBujAJ+oy7Ad9VKf/j8k/wCvY/zq3JIkSbpXVFHUscCs6e8tjdyEXEWDbkA7x1zSAi8Jf8ipY/7h/wDQjWxWD4Vu7ePwvYq88SsEOQXAI+Y1r/bbX/n5h/7+CgCeioPttr/z8w/9/BR9ttf+fmH/AL+CgCcjIIPes/Py26Hqlxj9DVn7ba/8/MP/AH8FUJry2W+jH2iLDShs7x6Gpe6LjqmjWoqD7ba/8/MP/fwUfbbX/n5h/wC/gqiCeioPttr/AM/MP/fwUfbbX/n5h/7+CgCeq8H/AB+XX+8v/oIpfttr/wA/MP8A38FV4L21+2XX+kw/eX/loP7ooAh1bw3p2sDdcw7Ju08fyuPx7/jWPjxH4b6f8TmwXt/y1QfzP6/hXTfbbX/n5h/7+Cj7ba/8/MP/AH8FYToRb5o6Puv61Oyni5xjyT96PZ/p1XyKOk+JNO1j5beby5/4oJflcfh3/CtWsHVtH0PV/nmkhiuOqzxSBWB/r+NZY1HWPDpxLPFrNiP4lceag/r+v1qPaVKf8RXXdfqjT2FGvrQlZ/yv9Hs/nY626/485v8Arm38qdB/x7x/7o/lWRb+JdL1TT5jBdIj+W2YpSFYceh6/hWhDe2v2eP/AEmH7o/5aD0rojKM1eLujiqU505cs1ZlqioPttr/AM/MP/fwUfbbX/n5h/7+CqIJ6Kg+22v/AD8w/wDfwUfbbX/n5h/7+CgBs3/H/bfR/wCQqzVCa9tft1t/pMPR/wDloPQVY+22v/PzD/38FAE9FQfbbX/n5h/7+Cj7ba/8/MP/AH8FAE9FQfbbX/n5h/7+Cj7ba/8APzD/AN/BQAln/wAt/wDrs1WKoWd7a/v/APSYf9c3/LQVY+22v/PzD/38FAE9FQfbbX/n5h/7+Cj7ba/8/MP/AH8FAE9V7/8A48ZPw/nS/bbX/n5h/wC/gqvf3tqbGTFzD2/5aD1oAv0VB9ttf+fmH/v4KPttr/z8w/8AfwUAT0VB9ttf+fmH/v4KPttr/wA/MP8A38FAE9Vz/wAhQf8AXE/+hCl+22v/AD8w/wDfwVXN7a/2mP8ASYf9Sf8AloP7woAv0VB9ttf+fmH/AL+Cj7ba/wDPzD/38FAE9FQfbbX/AJ+Yf+/gooAnooooAK5/xH4f1DW72ykt9QtIra1JkNpd2TTxyyZ+V2Cypnb2ByM89QMdBRQBTtItSSKEX93azOC3nNDatEHH8O0GRtuO+Sc+1ZKeG7+Se3h1DWPtem2s4nihe3PnsynKCSUuQwU88KpOFyTznoqKAAgHqM/WqM6L9sk+Uf8AHse3vV6qU/8Ax+Sf9ex/nQBS8Jqp8K2OVB+Q9v8AaNbOxf7o/Ksjwl/yKlj/ALh/9CNbFACbF/uj8qNi/wB0flS0UAJsX+6PyrP1BUjurOQqAPNwePY1o1WvAC1sCMjzh1/3TSauiouzJl8pxlNp+lO2L/dH5VC1pGTlcofVTSbbmL7rCQeh60rvqPli9mT7F/uj8qNi/wB0flUAuwpxMjIfpUyurj5GB+hpppicWtxdi/3R+VVoEX7ZdfKPvL2/2RVqq8H/AB+XX+8v/oIpkk+xf7o/KjYv90flS0UAJsX+6Pyo2L/dH5UtFAGHrnhnTtRhknMXkXKqWE0PytnHf1rOj0/xRpsaNZXlvqUOARHcLtbHpn/69dRdf8ec3/XNv5U6D/j3j/3R/KsJYeDfMtH5aHZDGVYx5JWkuzV/+CvkzmF8XGzYJr2j3Fkf+eirvQ/j/hmtqx1nStSx9iuoZGP8GcN/3yeavsodSrAMD1BHWsa+8JaNf5Z7NYXP8cHyH8hx+lTy14bNS9dPxX+RfPhKnxRcH5ar7nr+Js7F/uj8qNi/3R+Vcv8A8I9rmm86LrbSIOkF2Nwx6Z5/QCl/4SPWdN41vRJGQdZ7Q7h+XP6kUfWOX+JFr8V+AfU+f+DNS/B/c7fhc3pkX7fbfKOj9vYVZ2L/AHR+VYFv4q0e/u7do7xIiobcs3yYyPfit6OWOZN8Tq6noVORW0akJq8Xc5alGpSdqkWvUXYv90flRsX+6PypaKsyE2L/AHR+VGxf7o/KlooAq2aL+/8AlH+ubtVnYv8AdH5VBZ/8t/8Ars1WKAE2L/dH5UbF/uj8qWigBNi/3R+VVr9F+wyfKO3b3q1Ve/8A+PGT8P50AT7F/uj8qNi/3R+VLRQAmxf7o/KjYv8AdH5UtFACbF/uj8qrFF/tQfKP9Se3+0KtVXP/ACFB/wBcT/6EKAJ9i/3R+VGxf7o/KlooATYv90flRS0UAFFFFABRRWdrmu6f4c0qTUNWuFhhQcDq0jdlUfxMfQUAaNFR2863NrFPGCFlQOobqARmsmw8RvqV88VpouoNbJO8Bvi0Ai3ISrHHm78ZBH3aANeWNZU2sWA/2WIP5is6eyiF3IN0vFuT/rW9frWpVKf/AI/JP+vY/wA6AM3wraRyeF7JmMuSh6SsB949ga1/sMX96b/v83+NZ/hL/kVLH/cP/oRrYoAr/YYv703/AH+b/Gj7DF/em/7/ADf41YooAr/YYv703/f5v8arXVlFvtvmm/1w/wCWzeh960ar3f37b/rsP5GgA+wxf3pv+/zf40fYYv703/f5v8asUUAVjYQkYJlI/wCuzf41E2k2x5UzIfUSt/jV6ik0mNSa2M86e8f3ZJJB6GZh/Wq8Eca3dz5qXC/MvSVv7o962Krwf8fl1/vL/wCgilbsVzJ7ohWO0b/lrMPrK/8AjUi2lu33ZJD9J2/xqw0aN95VP1FRtawt/Dj6Gj3g90b9hi/vTf8Af5v8aPsMX96b/v8AN/jR9lK/6uV1/HNGy5X7sit9RRd9g5V0ZDdWMX2Ob5pv9W3/AC2b0+tOhsYvs8fzTfdH/LZvT60XUlwtpNvhBHltyp9qWG5Igj3QyD5R29qOZByMf9hi/vTf9/m/xo+wxf3pv+/zf40v2yLvuH1FOF1Cf4/zFPmQuSXYZ9hi/vTf9/m/xo+wxf3pv+/zf41KJoj0kX86cGB6EH6GncVmYl94d0u7voPtNqJC4bcWdsnj1zmq0ngXSwxeykurN+xhmP8AXJrcm/4/7b6P/IVZrGVClN3lFHRTxdemrQm0vXT7jk5PDOuwc2PiGWQdkuAf55NMx4os/wDj40+K+QdWgunUn82/pXX0VH1dL4ZNfP8Azua/XJS/iQjL5W/KxyH/AAk0Nt/yFdJ1Sz9W3sy/mSKu23iDw7dY2akyH0lldMfieK6KqVzo2m3mftNhbyE/xGMZ/PrRy147ST9V/l/kHtMJL4oOPo/0a/Ur6fFZzrMYbhpAZWIKXDHI/A1d+wxf3pv+/wA3+Nc+ngrRbgzHyJIisrAGOU8D8c07/hDBD/x46zqVv7edkfpijnrLeCfo/wDNB7LCy2qNesf8mze+wxf3pv8Av83+NH2GL+9N/wB/m/xrB/sLxFb/APHr4jaT2mgB/U5o2+MrfpJpt2PcFSf5U/bSXxQf4P8AJh9Vg/hqxf3r80b32GL+9N/3+b/Gq1/ZRCxk+abt/wAtm9frWV/bPia3/wCPnw+ko9YZxz/OobrxZMLV0vdC1C3zj5vL3Dr68UfWafW69Uw+o1n8Nn6ST/U6T7DF/em/7/N/jR9hi/vTf9/m/wAawh480gHEy3UJ/wCmkP8Aganj8a6BJ/y/bT6NE4/pTWJov7S+8l4HFL/l2/uNb7DF/em/7/N/jR9hi/vTf9/m/wAapR+J9Fk+7qduP959v86tR6tp0v8Aqr+1f/dmU/1q1Ug9mjGVCrH4otfJj/sMX96b/v8AN/jVY2UX9pj5pv8AUn/ls3r9avLPEylllQqBkkMMCuUbx3pI1rGJjCF8vzgo29euM5xSnWp07c7tcqlhq1a/s4t2Om+wxf3pv+/zf40fYYv703/f5v8AGp1ZXRXQhlYZBB4Ipa1Ocr/YYv703/f5v8aKsUUAFFFFABWR4phik8MalLJEjSRWc3luVBKZjIOD24rXooAoabcQx6Xp0MkqLLLbr5aFgGfCAnA74riNObTLG6soPDOp6g2rvfk3enXd40jpGzkymSDdsjAByGCjnbgndz6NRTAZKsjJiJwjepXI/Ks6eO6+1yZuI8/Zzk+V2z/vVqVSn/4/JP8Ar2P86QGb4VjuT4XsjHPGq7DgGLOPmPfNa/lXf/PzH/35/wDsqz/CX/IqWP8AuH/0I1sUAV/Ku/8An5j/AO/P/wBlR5V3/wA/Mf8A35/+yqxRQBX8q7/5+Y/+/P8A9lVa6ju99t/pMf8Arh/yx9j/ALVaNV7v79t/12H8jQAeVd/8/Mf/AH5/+yo8q7/5+Y/+/P8A9lViigCv5V3/AM/Mf/fn/wCyo8q7/wCfmP8A78//AGVWKKAK/lXf/PzH/wB+f/sqrQR3f2y6/wBJj+8v/LH/AGR/tVo1Xg/4/Lr/AHl/9BFAB5V3/wA/Mf8A35/+yo8q7/5+Y/8Avz/9lViigCv5V3/z8x/9+f8A7Kjyrv8A5+Y/+/P/ANlViigCjdR3f2Ob/SY/9W3/ACx9v96nQx3f2eP/AEmP7o/5Y+3+9U91/wAec3/XNv5U6D/j3j/3R/KgCHyrv/n5i/78/wD2VIbe4PWaE/WD/wCyq1RQBSNnMf8AlrD+EJ/+Kpp0+U/8t4x/2yP/AMVV+ilZFc0u5kS2FwL23Au1GQ/SP2HvVj7FeDpf/wDkI/8AxVTTf8f9t9H/AJCrNLlQ+eRR+yX46X6/jD/9lR5GpDpexH6w/wD16vUUcqDnZR8rUx/y8wH/ALZH/Gk26mP+WsB/7Z//AGVX6KLBzeRkWn9pDzsNF/rWz+7/APsqnzqXrH/37/8Ar1Ys/wDlv/12arFFn3DmXYz9+odyn/fr/wCvR5l/3Yf9+T/jWhRRZ9w5l2M/zb3u/wD5AP8A8VVe+luvsUmZB2/5Ye/+9WxVe/8A+PGT8P50WfcLx7FczXBGDKCPe3/+yqvJbxy/62O3f/eswf8A2atiik433GpqO35nPSaNp8n37Oy/CyA/kaqyeFtIk+9bRD/djdf5PXV0Vm6MHul9xvHFVY/DJr5s4ubwXpTo3lO0TEHBVWOD+LGuTTwtq8l/9kS1LNn74I2Yz1z6frXsFVz/AMhQf9cT/wChCuargKVRrp6HdQzjEUU1fmv3dyvY2N1Zafb2q3UZEMSxgmE84GP71WPKu/8An5j/AO/P/wBlViiu9JJWR48pOTbfUr+Vd/8APzH/AN+f/sqKsUUxBRRRQAUUUUAFFFFADJWZEzHGZD/dBA/nWdPPP9rkP2Rwfs543rxz9a1KpT/8fkn/AF7H+dAGb4WmmXwvZBLVnAQ4YOoz8x961vtFx/z5v/32v+NUPCX/ACKlj/uH/wBCNbFAFb7Rcf8APm//AH2v+NH2i4/583/77X/GrNFAFb7Rcf8APm//AH2v+NV7qeffbf6G/wDrh/GvofetGq939+2/67D+RoAT7Rcf8+b/APfa/wCNH2i4/wCfN/8Avtf8as0UAVvtFx/z5v8A99r/AI0faLj/AJ83/wC+1/xqzRQBW+0XH/Pm/wD32v8AjVeCef7Zdf6G/wB5f41/uj3rRqvB/wAfl1/vL/6CKAE+0XH/AD5v/wB9r/jR9ouP+fN/++1/xqzRQBW+0XH/AD5v/wB9r/jR9ouP+fN/++1/xqzRQBRuri4+xzf6G/8Aq2/jX0+tOhuLj7PH/ob/AHR/Gvp9anuv+POb/rm38qdB/wAe8f8Auj+VAEP2i4/583/77X/Gj7Rcf8+b/wDfa/41ZooArfaLj/nzf/vtf8aPtFx/z5v/AN9r/jVmigDOmnn+3W3+hv0f+NfQe9WPtFx/z5v/AN9r/jRN/wAf9t9H/kKs0AVvtFx/z5v/AN9r/jR9ouP+fN/++1/xqzRQBW+0XH/Pm/8A32v+NH2i4/583/77X/GrNFAGdaTz/v8A/Q3/ANc38a/41Y+0XH/Pm/8A32v+NLZ/8t/+uzVYoArfaLj/AJ83/wC+1/xo+0XH/Pm//fa/41ZooArfaLj/AJ83/wC+1/xqvfzz/YZP9DcdP419frWjVe//AOPGT8P50AJ9ouP+fN/++1/xo+0XH/Pm/wD32v8AjVmigCt9ouP+fN/++1/xo+0XH/Pm/wD32v8AjVmigCt9ouP+fN/++1/xquZ5/wC0x/ob/wCpP8a+v1rRquf+QoP+uJ/9CFACfaLj/nzf/vtf8aPtFx/z5v8A99r/AI1ZooArfaLj/nzf/vtf8aKs0UAFFFFABXO6wj6p4os9IkuLmC0FrJdSi1uHgeRgyqo3oQwAyTgEZ4roqydW0i5u7621DS7yOzvrdHiDzQGaN42wSrKGU9VUghh070wI/C1zPPpMsNzM872d1Na+dJ951RyFLHucYye5rm9CuTHqKXniGz8QWktxfypBcXN9ItsSZGEaeSJuAVwBujAJ+oz1mlaVLpVnDbpciYbnkuZJI/nmkcliwIOF+YnjB4wPes9PDd/JPbw6hrH2vTbWcTxQvbnz2ZTlBJKXIYKeeFUnC5J5yAb8sqQpvlbavqazp762N3IRKuDbkD861KpT/wDH5J/17H+dIDN8K3lvH4XskeUBghyP+BGtf+0LX/nstZ/hL/kVLH/cP/oRrYoAr/2ha/8APZaP7Qtf+ey1YooAr/2ha/8APZarXV/al7b98v8Arh/I1o1Xu/v23/XYfyNAB/aFr/z2Wj+0LX/nstWKKAK/9oWv/PZaP7Qtf+ey1YooAr/2ha/89lqtBf2v2y6/fL95f/QRWjVeD/j8uv8AeX/0EUAH9oWv/PZaP7Qtf+ey1YooAr/2ha/89lo/tC1/57LViigCjdX9qbOb98v+rb+VOhv7X7PH++X7o/lU91/x5zf9c2/lToP+PeP/AHR/KgCL+0LX/nstH9oWv/PZasUUAV/7Qtf+ey0f2ha/89lqxRQBnTX9r9utv3y9H/kKs/2ha/8APZaSb/j/ALb6P/IVZoAr/wBoWv8Az2Wj+0LX/nstWKKAK/8AaFr/AM9lo/tC1/57LViigDOtL+1Hn/vl/wBc1Wf7Qtf+ey0Wf/Lf/rs1WKAK/wDaFr/z2Wj+0LX/AJ7LViigCv8A2ha/89lqtf39qbGTEy9v51o1Xv8A/jxk/D+dAB/aFr/z2Wj+0LX/AJ7LViigCv8A2ha/89lo/tC1/wCey1YooAr/ANoWv/PZarG/tf7TB85f9Sf51o1XP/IUH/XE/wDoQoAP7Qtf+ey0f2ha/wDPZasUUAV/7Qtf+ey0VYooAKKKKACiiigAooooAKoXcixXbGTIDQFQcHk56VfooAwvDVxHaeHLOC4YpIiEMpU8cmtT+0Lb/nr/AOOn/Cn2kTQWcUT43IoBxU1AFb+0Lb/nr/46f8KP7Qtv+ev/AI6f8Ks0UAVv7Qtv+ev/AI6f8Kgub63Z7fEn3ZQT8p9DWhUNxE0rQlcfJIGOfTBH9aAGf2hbf89f/HT/AIUf2hbf89f/AB0/4VZooArf2hbf89f/AB0/4Uf2hbf89f8Ax0/4VZooArf2hbf89f8Ax0/4VBDfW4urkmThmXHyn+6K0KhiiZLidzjEhBH4DFADP7Qtv+ev/jp/wo/tC2/56/8Ajp/wqzRQBW/tC2/56/8Ajp/wo/tC2/56/wDjp/wqzRQBSub62a1lAk5KED5T6UsN/bCBAZOQo/hPpVmdDJbyIvVkIGfpSxKUhRT1VQDQBB/aFt/z1/8AHT/hR/aFt/z1/wDHT/hVmigCt/aFt/z1/wDHT/hR/aFt/wA9f/HT/hVmigDPlvrc3luwk4UNn5T6VP8A2hbf89f/AB0/4U+SJnuoZBjagbP4ipqAK39oW3/PX/x0/wCFH9oW3/PX/wAdP+FWaKAK39oW3/PX/wAdP+FH9oW3/PX/AMdP+FWaKAM+1vrdfO3SYzKxHymp/wC0Lb/nr/46f8KfbxNF5u7HzyFhj0NTUAVv7Qtv+ev/AI6f8KP7Qtv+ev8A46f8Ks0UAVv7Qtv+ev8A46f8Kgvb63ezkVZMk4/hPrWhUN3E01q8aY3NjGfrQAz+0Lb/AJ6/+On/AAo/tC2/56/+On/CrNFAFb+0Lb/nr/46f8KP7Qtv+ev/AI6f8Ks0UAVv7Qtv+ev/AI6f8Kg+3W/9ohvM+XyiM7T61oVD5TfbhLxt8vb+Oc0AM/tC2/56/wDjp/wo/tC2/wCev/jp/wAKs0UAVv7Qtv8Anr/46f8ACirNFABRRRQAUUUUAFFFFABWfrWqf2TYLKkBuJ5ZVggh3bfMkY4ALdh3JwcAHg9K0Kw/FMEzWlleW8MlwbC8juXiiXc7IMhto6kgMTgcnHFAE+katcXtxdWepWaWd9alS8cUxljdGB2srlVJHBHKggg+xOrXPaGW1DxFqWsrb3NvbSxQ20P2mFoXk2FyzbHAYDL4GQM4PbBroaACiiigArC1/wAU22hXFpa/Z5rq5uZo02RKdsSu4Te7YwoyeAeSeB3I3aw/F1vNc6PCltDJK4vrVysaliFEyEnjsACTTQG5RRRSAKKKKAGyP5cbOQzBQThRkn6DvWBaeIdQN7Ypq2kLYwaixW2IuS8qttLhZYygCHaD0ZsEY9635ZBFC8hVmCKWIRSxOPQDqfauL0HURrfiGLUtZt9UgufmSxspdLuY4rRSOWeRowhkYDk5wPurnJLMDtqKKKQBRRRQBR1W9urOGIafYte3M0gjRC5SNeCSzuAdq4HXB5wMc0zRtUfU4JxcQLb3VrMYLiJJPMVXAB+VsDIIYHJAPPQUmt3z2FrG32K5uraR/LuGtN5lhQg/OqoN7c4Hy8jOR0rO8H2zW1vfiCG5h017kvZreIyzEEDezb/nOX3EF/m/DFPoB0dFFFIAooooAxfEPiWDQGsomia4uLy4jiWJWxtVnCmQnso3D6kgd62q4PxR4d14zXN9ZX9rcfar21KxtprySQxpKu1dyygbFOWPyjOTyOo7tAwRRIQzY+YqMAn6UwFooopAFQXt0ljYXF3L9yCJpG57AZ/pU9Udbg+06DfQfY/t3mQOv2bzTF52R93ePu56ZoAraHqGs6jDFcanpllZQTQrInk3zzOCQCAymJAOD6mteuM8P2FjH4it5vDGjzaRYR2rpeq1i9osrkrsG1lXey4b5xnrjJzXZ02AUUUUgCsjVdYuLXULfTdLskvL+eNpQs0xhijjUgFmcKxHJAACnPtiteuT8SaXYt4kg1LW9Nk1LTjZtbPElq90FfeGUtEoYsOuDtO0jtmmgOh0u9lv9OjnubSWzmJKyQSjlGBIODgZHGQe4wat1heD7a4tfDyx3EU0EZmka2gn+/DAXPloe4wuODyOnat2kAUUUUAFczqev6/pt/a250XTpxd3PkwLHqcnmsvUuVMGAFX5j83HTJ4z01YcFtLceObq8uIWEVpaRwWzshAJclpCp7/dQcelMDcooopAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('GRU_Model_train_Loss_Plot.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model 2 Validation CTC Loss Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('GRU_Model_Val_Loss_Plot.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Training Time for Model 2 lower than Model 1 \n",
    "2. Train and Validation CTC loss for Model 1 is comparatively lower than the Train, Validation CTC loss for Model 2 after 50 epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13. Model Output Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.1. Best Path Decoding "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Function Takes the final 40x37 output matrix from the model, and takes the argmax of the matrix across each column (which returns a value between 0 to 36 (ctc_blank) both included). The outputs are then merged for repeated values and gives a list of integers. The Final output integers is then converted to final output string text and it is returned by the function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://keras.io/examples/image_ocr/\n",
    "#https://github.com/qjadud1994/CRNN-Keras\n",
    "def decode_label(out):\n",
    "    \"\"\"\n",
    "    Takes the predicted ouput matrix from the Model and returns the output text for the image\n",
    "    \"\"\"\n",
    "    # out : (1, 42, 37)\n",
    "    # discarding first 2 outputs of RNN as they tend to be garbage \n",
    "    out_best = list(np.argmax(out[0,2:], axis=1))\n",
    "\n",
    "    out_best = [k for k, g in itertools.groupby(out_best)]  # remove overlap value\n",
    "\n",
    "    outstr=words_from_labels(out_best)\n",
    "    return outstr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.2. Test Output Prediction Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Function Takes in Following Inputs:\n",
    "1. **model:** The Best Model obtained after training\n",
    "2. **test_img_names:** The Image folder path where the input image is located\n",
    "3. **test_labels:** The Ground Truth Text Labels of Corresponding input Images\n",
    "\n",
    "<p> The function reads the images in the folder path and  resizes it to 32x100 dimension,single channel gray scale image.The processed image is passed to to best model which predicts the output which is a 1x48x37 matrix. The output Matrix is passed to CTC Best path decoding function which returns the predicted text word. The function then compares this predicted word text with the expected Ground truth Label and calculates Accuracy and Letter-Accuracy across the entire test data and returns accuracy, letter accuracy, letter count and letter mis match count.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_data_output_Prediction(model,test_img_names,test_labels):\n",
    "    \"\"\"\n",
    "    Takes the best model, test data image paths, test data groud truth labels and pre-processes the input image to \n",
    "    appropriate format for the model prediction, takes the predicted output matrix and uses best path decoding to \n",
    "    generate predicted text and compares with ground truth text for the input and ouputs the final accuracy,\n",
    "    letter accuracy and  letter count across the entire test set of images, it also returns list of letter mis-match\n",
    "    count for each test point in the whole test set of images\n",
    "    \"\"\"\n",
    "    start=datetime.now()\n",
    "    accuracy=0\n",
    "    letter_acc=0\n",
    "    letter_cnt=0\n",
    "    count=0\n",
    "    letter_mis_match=[]\n",
    "    for i in range(len(test_labels)):\n",
    "        test_img=cv2.imread(test_img_names[i])\n",
    "        test_img_resized=cv2.resize(test_img,(170,32))\n",
    "        test_image=test_img_resized[:,:,1]\n",
    "        test_image=test_image.T\n",
    "        test_image=np.expand_dims(test_image,axis=-1)\n",
    "        test_image=np.expand_dims(test_image, axis=0)\n",
    "        test_image=test_image/255\n",
    "        model_output=model.predict(test_image)\n",
    "        predicted_output=decode_label(model_output)\n",
    "        actual_output=test_labels[i]\n",
    "        count+=1\n",
    "        mis_match=0\n",
    "        for j in range(min(len(predicted_output),len(actual_output))):\n",
    "            if predicted_output[j]==actual_output[j]:\n",
    "                letter_acc+=1\n",
    "            else:\n",
    "                mis_match+=1\n",
    "        letter_cnt+=max(len(predicted_output),len(actual_output))\n",
    "        letter_mis_match.append(mis_match)\n",
    "        if actual_output==predicted_output:\n",
    "            accuracy+=1\n",
    "        if (count%1000)==0:\n",
    "            print(\"Processed \",count,\" Images\")\n",
    "    print(\"Time Taken for Processing: \",datetime.now()-start)\n",
    "    return accuracy,letter_acc,letter_cnt,letter_mis_match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.3. Model 1 Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Output Prediction, load the model achitecture  defined above for Predict stage which takes in the image as input and outputs a 48x37 matrix as ouput for each input image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Image_text_recogniser_model_1('predict')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Best Weights for the Model are stored in BestLSTMModelWeights Folder, loading the stored best weights for Model 1 for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('Final_LSTM_Model_Best_Weights/Best_Img_recog_LSTM_Adam_model_run_weights.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Synth Text Validation Data Prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_img_names=val_data['ImageName'].values\n",
    "val_labels=val_data['Labels'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed  1000  Images\n",
      "Processed  2000  Images\n",
      "Processed  3000  Images\n",
      "Processed  4000  Images\n",
      "Processed  5000  Images\n",
      "Processed  6000  Images\n",
      "Processed  7000  Images\n",
      "Processed  8000  Images\n",
      "Processed  9000  Images\n",
      "Processed  10000  Images\n",
      "Processed  11000  Images\n",
      "Processed  12000  Images\n",
      "Time Taken for Processing:  0:39:23.430397\n"
     ]
    }
   ],
   "source": [
    "synth_val_accuracy,synth_val_letter_acc,synth_val_letter_cnt,synth_val_mis_match=test_data_output_Prediction(model,val_img_names,val_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Output Accuracy:  86.47500000000001  %\n",
      "Model Output Letter Accuracy:  94.01335219000873  %\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Output Accuracy: \",(synth_val_accuracy/len(val_labels))*100, \" %\")\n",
    "print(\"Model Output Letter Accuracy: \",(synth_val_letter_acc/synth_val_letter_cnt)*100, \" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1_val_mis_match_dict=Counter(synth_val_mis_match)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model 1 Validation Data Prediction Analysis upto 4 Character Mis-Matches**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "mis_match_cnts_1=[]\n",
    "for i in range(5):\n",
    "    mis_match_cnts_1.append(model_1_val_mis_match_dict[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mis_match_character_analysis_plot(mis_match_counts,num_values):\n",
    "    \"\"\"\n",
    "    Takes mis-match counts upto 4 characters of the predicted output, total number of values and\n",
    "    plots the percentage of number of mis-match characters between predicted and actual labels\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(10,6))\n",
    "    indices=np.arange(len(mis_match_counts))\n",
    "    counts=np.array(mis_match_counts)\n",
    "    percent=(counts/num_values)*100\n",
    "    plt.bar(indices,percent)\n",
    "    plt.xlabel('Number of Mis-Match Characters',fontsize=10)\n",
    "    plt.ylabel('Percentages',fontsize=10)\n",
    "    plt.title('Percentages of Number of Mis-Match Characters',fontsize=12)\n",
    "    plt.xticks(indices,indices)\n",
    "    plt.show()\n",
    "    for i in range(len(indices)):\n",
    "        print(i,\" Mis-Match Characters Percentage: \",np.round(percent[i],2),\" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0  Mis-Match Characters Percentage:  86.58  %\n",
      "1  Mis-Match Characters Percentage:  5.78  %\n",
      "2  Mis-Match Characters Percentage:  2.15  %\n",
      "3  Mis-Match Characters Percentage:  1.18  %\n",
      "4  Mis-Match Characters Percentage:  1.07  %\n"
     ]
    }
   ],
   "source": [
    "mis_match_character_analysis_plot(mis_match_cnts_1,12000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Synth Text Test Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_img_names=test_data['ImageName'].values\n",
    "test_labels=test_data['Labels'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed  1000  Images\n",
      "Processed  2000  Images\n",
      "Processed  3000  Images\n",
      "Processed  4000  Images\n",
      "Processed  5000  Images\n",
      "Processed  6000  Images\n",
      "Processed  7000  Images\n",
      "Processed  8000  Images\n",
      "Processed  9000  Images\n",
      "Processed  10000  Images\n",
      "Processed  11000  Images\n",
      "Processed  12000  Images\n",
      "Processed  13000  Images\n",
      "Processed  14000  Images\n",
      "Processed  15000  Images\n",
      "Time Taken for Processing:  0:21:50.478253\n"
     ]
    }
   ],
   "source": [
    "synth_test_accuracy,synth_test_letter_acc,synth_test_letter_cnt,synth_test_mis_match=test_data_output_Prediction(model,test_img_names,test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Output Accuracy:  87.08666666666667  %\n",
      "Model Output Letter Accuracy:  94.48166697375254  %\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Output Accuracy: \",(synth_test_accuracy/len(test_labels))*100, \" %\")\n",
    "print(\"Model Output Letter Accuracy: \",(synth_test_letter_acc/synth_test_letter_cnt)*100, \" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_1_test_mis_match_dict=Counter(synth_test_mis_match)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model 1 Test Data Prediction Analysis upto 4 Character Mis-Matches**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "mis_match_cnts_2=[]\n",
    "for i in range(5):\n",
    "    mis_match_cnts_2.append(model_1_test_mis_match_dict[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0  Mis-Match Characters Percentage:  87.19  %\n",
      "1  Mis-Match Characters Percentage:  5.85  %\n",
      "2  Mis-Match Characters Percentage:  2.1  %\n",
      "3  Mis-Match Characters Percentage:  1.17  %\n",
      "4  Mis-Match Characters Percentage:  0.91  %\n"
     ]
    }
   ],
   "source": [
    "mis_match_character_analysis_plot(mis_match_cnts_2,15000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13.4. Model 2 Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Output Prediction, load the model achitecture  defined above for Predict stage which takes in the image as input and outputs a 48x37 matrix as ouput for each input image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2=Image_text_recogniser_model_2('predict')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Best Weights for the Model are stored in BestGRUModelWeights Folder, loading the stored best weights for Model 2 for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2.load_weights('Final_GRU_Model_Best_Weights/Best_Img_recog_GRU_RAdam_model_run_weights.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Synth Text Validation Data Prediction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_img_names=val_data['ImageName'].values\n",
    "val_labels=val_data['Labels'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed  1000  Images\n",
      "Processed  2000  Images\n",
      "Processed  3000  Images\n",
      "Processed  4000  Images\n",
      "Processed  5000  Images\n",
      "Processed  6000  Images\n",
      "Processed  7000  Images\n",
      "Processed  8000  Images\n",
      "Processed  9000  Images\n",
      "Processed  10000  Images\n",
      "Processed  11000  Images\n",
      "Processed  12000  Images\n",
      "Time Taken for Processing:  0:14:31.055968\n"
     ]
    }
   ],
   "source": [
    "synth_val_accuracy,synth_val_letter_acc,synth_val_letter_cnt,synth_val_mis_match=test_data_output_Prediction(model_2,val_img_names,val_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Output Accuracy:  81.86666666666666  %\n",
      "Model Output Letter Accuracy:  92.10611116308563  %\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Output Accuracy: \",(synth_val_accuracy/len(val_labels))*100, \" %\")\n",
    "print(\"Model Output Letter Accuracy: \",(synth_val_letter_acc/synth_val_letter_cnt)*100, \" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2_val_mis_match_dict=Counter(synth_val_mis_match)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model 2 Validation Data Prediction Analysis upto 4 Character Mis-Matches**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "mis_match_cnts_3=[]\n",
    "for i in range(5):\n",
    "    mis_match_cnts_3.append(model_2_val_mis_match_dict[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0  Mis-Match Characters Percentage:  82.06  %\n",
      "1  Mis-Match Characters Percentage:  7.32  %\n",
      "2  Mis-Match Characters Percentage:  2.88  %\n",
      "3  Mis-Match Characters Percentage:  1.85  %\n",
      "4  Mis-Match Characters Percentage:  1.41  %\n"
     ]
    }
   ],
   "source": [
    "mis_match_character_analysis_plot(mis_match_cnts_3,12000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Synth Text Test Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_img_names=test_data['ImageName'].values\n",
    "test_labels=test_data['Labels'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed  1000  Images\n",
      "Processed  2000  Images\n",
      "Processed  3000  Images\n",
      "Processed  4000  Images\n",
      "Processed  5000  Images\n",
      "Processed  6000  Images\n",
      "Processed  7000  Images\n",
      "Processed  8000  Images\n",
      "Processed  9000  Images\n",
      "Processed  10000  Images\n",
      "Processed  11000  Images\n",
      "Processed  12000  Images\n",
      "Processed  13000  Images\n",
      "Processed  14000  Images\n",
      "Processed  15000  Images\n",
      "Time Taken for Processing:  0:18:08.747058\n"
     ]
    }
   ],
   "source": [
    "synth_test_accuracy,synth_test_letter_acc,synth_test_letter_cnt,synth_test_mis_match=test_data_output_Prediction(model_2,test_img_names,test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Output Accuracy:  82.43333333333334  %\n",
      "Model Output Letter Accuracy:  92.63425739922326  %\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Output Accuracy: \",(synth_test_accuracy/len(test_labels))*100, \" %\")\n",
    "print(\"Model Output Letter Accuracy: \",(synth_test_letter_acc/synth_test_letter_cnt)*100, \" %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_2_test_mis_match_dict=Counter(synth_test_mis_match)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Model 2 Test Data Prediction Analysis upto 4 Character Mis-Matches**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "mis_match_cnts_4=[]\n",
    "for i in range(5):\n",
    "    mis_match_cnts_4.append(model_2_test_mis_match_dict[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0  Mis-Match Characters Percentage:  82.73  %\n",
      "1  Mis-Match Characters Percentage:  7.45  %\n",
      "2  Mis-Match Characters Percentage:  2.82  %\n",
      "3  Mis-Match Characters Percentage:  1.76  %\n",
      "4  Mis-Match Characters Percentage:  1.33  %\n"
     ]
    }
   ],
   "source": [
    "mis_match_character_analysis_plot(mis_match_cnts_4,15000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14. Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Model 1:** Model 1 Architecture consists of Convulution Layers, LSTM Units for RNN and uses Adam Optimizer\n",
    "2. **Model 2:** Model 2 Architecture consists of Convulution Layers, GRU Units for RNN and uses RAdam Optimizer\n",
    "3. **Val:** Denotes Synth Text Validation Data containing 12000 Images\n",
    "4. **Test:** Denotes Synth Text Test Data containing 15000 Images\n",
    "5. **0 MM(%):** Denotes % of points out of total points which have 0 character mis-match between Predicted and Actual Labels\n",
    "6. **1 MM(%):** Denotes % of points out of total points which have 1 character mis-match between Predicted and Actual Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+------+----------+-----------------+---------+----------+\n",
      "|  Model  | Data | Accuracy | Letter Accuracy | 0 MM(%) | 1 MM (%) |\n",
      "+---------+------+----------+-----------------+---------+----------+\n",
      "| Model 1 | Val  |  86.47   |      94.01      |  86.58  |   5.78   |\n",
      "| Model 1 | Test |  87.98   |      94.48      |  87.19  |   5.85   |\n",
      "| Model 2 | Val  |  81.86   |       92.1      |  82.06  |   7.32   |\n",
      "| Model 2 | Test |  82.43   |      92.63      |  82.73  |   7.45   |\n",
      "+---------+------+----------+-----------------+---------+----------+\n"
     ]
    }
   ],
   "source": [
    "from prettytable import PrettyTable\n",
    "\n",
    "pt = PrettyTable()\n",
    "\n",
    "pt.field_names = [\"Model\", \"Data\", \"Accuracy\",\"Letter Accuracy\", \"0 MM(%)\", \"1 MM (%)\"]\n",
    "\n",
    "pt.add_row([\"Model 1\", \"Val\", 86.47, 94.01, 86.58, 5.78])\n",
    "\n",
    "pt.add_row([\"Model 1\", \"Test\", 87.98, 94.48, 87.19, 5.85])\n",
    "\n",
    "pt.add_row([\"Model 2\", \"Val\", 81.86, 92.1, 82.06, 7.32])\n",
    "\n",
    "pt.add_row([\"Model 2\", \"Test\", 82.43, 92.63, 82.73, 7.45])\n",
    "\n",
    "print(pt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15. Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The 2 Image Text Recognition Model Architectures are Trained on 200000 Synth Text Images of Variable length Labels which are different from Validation Data containing 12000 images and Test Data Containing 15000 images.\n",
    "2. Model 1 Trained on 200000 images from Synth Text Images performs reasonably well on Unseen 15000 Test Images of Variable length labels with an accuracy of ~88% and letter accuracy of ~94%\n",
    "3. Model 2 also Trained on same 200000 images has an accuracy of ~82% and letter accuracy of ~93% on 15000 Test Images of Variable length labels\n",
    "4. Both the Models have high percentage (>82%) of 0 mis-match character points between Actual and Predicted Labels "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}