Detection and Recognition of Words.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "from random import *\n",
    "import re\n",
    "import cv2 \n",
    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "import pytesseract\n",
    "from PIL import Image\n",
    "import matplotlib.image as mpimg\n",
    "from pytesseract import Output\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files/Tesseract-OCR/tesseract'\n",
    "TESSDATA_PREFIX = 'C:/Program Files/Tesseract-OCR'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get grayscale image\n",
    "def get_grayscale(image):\n",
    "    return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "# noise removal\n",
    "def remove_noise(image):\n",
    "    return cv2.medianBlur(image,5)\n",
    " \n",
    "#thresholding\n",
    "def thresholding(image):\n",
    "    return cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]\n",
    "\n",
    "#dilation\n",
    "def dilate(image):\n",
    "    kernel = np.ones((5,5),np.uint8)\n",
    "    return cv2.dilate(image, kernel, iterations = 1)\n",
    "    \n",
    "#erosion\n",
    "def erode(image):\n",
    "    kernel = np.ones((5,5),np.uint8)\n",
    "    return cv2.erode(image, kernel, iterations = 1)\n",
    "\n",
    "#opening - erosion followed by dilation\n",
    "def opening(image):\n",
    "    kernel = np.ones((5,5),np.uint8)\n",
    "    return cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)\n",
    "\n",
    "#canny edge detection\n",
    "def canny(image):\n",
    "    return cv2.Canny(image, 100, 200)\n",
    "\n",
    "#skew correction\n",
    "def deskew(image):\n",
    "    coords = np.column_stack(np.where(image > 0))\n",
    "    angle = cv2.minAreaRect(coords)[-1]\n",
    "    if angle < -45:\n",
    "        angle = -(90 + angle)\n",
    "    else:\n",
    "        angle = -angle\n",
    "    (h, w) = image.shape[:2]\n",
    "    center = (w // 2, h // 2)\n",
    "    M = cv2.getRotationMatrix2D(center, angle, 1.0)\n",
    "    rotated = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)\n",
    "    return rotated\n",
    "\n",
    "#template matching\n",
    "def match_template(image, template):\n",
    "    return cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OCR Images\\OCR Image 1.png\n",
      "OCR Images\\OCR Image 10.png\n",
      "OCR Images\\OCR Image 11.png\n",
      "OCR Images\\OCR Image 12.png\n",
      "OCR Images\\OCR Image 13.png\n",
      "OCR Images\\OCR Image 14.png\n",
      "OCR Images\\OCR Image 15.png\n",
      "OCR Images\\OCR Image 18.png\n",
      "OCR Images\\OCR Image 21.png\n",
      "OCR Images\\OCR Image 23.png\n",
      "OCR Images\\OCR Image 25.png\n",
      "OCR Images\\OCR Image 6.png\n",
      "OCR Images\\OCR Image 7.png\n",
      "OCR Images\\OCR Image 8.png\n",
      "OCR Images\\OCR Image 16.jpg\n",
      "OCR Images\\OCR Image 17.jpg\n",
      "OCR Images\\OCR Image 19.jpg\n",
      "OCR Images\\OCR Image 2.jpg\n",
      "OCR Images\\OCR Image 20.jpg\n",
      "OCR Images\\OCR Image 22.jpg\n",
      "OCR Images\\OCR Image 24.jpg\n",
      "OCR Images\\OCR Image 26.jpg\n",
      "OCR Images\\OCR Image 4.jpg\n",
      "OCR Images\\OCR Image 3.jfif\n",
      "OCR Images\\OCR Image 5.jfif\n",
      "OCR Images\\OCR Image 9.jfif\n"
     ]
    }
   ],
   "source": [
    "files = glob.glob (\"OCR Images/*.png\")\n",
    "for myFile in files:\n",
    "    print(myFile)\n",
    "    \n",
    "files = glob.glob (\"OCR Images/*.jpg\")\n",
    "for myFile in files:\n",
    "    print(myFile)\n",
    "    \n",
    "files = glob.glob (\"OCR Images/*.jfif\")\n",
    "for myFile in files:\n",
    "    print(myFile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image = cv2.imread(\"OCR Images\\OCR Image 1.png\")\n",
    "plt.imshow(image)\n",
    "b,g,r = cv2.split(image)\n",
    "rgb_img = cv2.merge([r,g,b])\n",
    "plt.imshow(rgb_img)\n",
    "plt.title('ORIGINAL IMAGE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gray = get_grayscale(image)\n",
    "thresh = thresholding(gray)\n",
    "opening = opening(gray)\n",
    "canny = canny(gray)\n",
    "images = {'gray': gray, \n",
    "          'thresh': thresh, \n",
    "          'opening': opening, \n",
    "          'canny': canny}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x936 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(13,13))\n",
    "ax = []\n",
    "\n",
    "rows = 2\n",
    "columns = 2\n",
    "keys = list(images.keys())\n",
    "for i in range(rows*columns):\n",
    "    ax.append( fig.add_subplot(rows, columns, i+1) )\n",
    "    ax[-1].set_title('Original - ' + keys[i]) \n",
    "    plt.imshow(images[keys[i]], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------\n",
      "TESSERACT OUTPUT --> ORIGINAL IMAGE\n",
      "-----------------------------------------\n",
      "££\n",
      "| just had a ton of energy all of\n",
      "a sudden and I’m like pacing\n",
      "back and forth in my room\n",
      "a\n",
      "\n",
      "-----------------------------------------\n",
      "TESSERACT OUTPUT --> THRESHOLDED IMAGE\n",
      "-----------------------------------------\n",
      "££\n",
      "| just had a ton of energy all of\n",
      "a sudden and I’m like pacing\n",
      "back and forth in my room\n",
      "a\n",
      "\n",
      "-----------------------------------------\n",
      "TESSERACT OUTPUT --> OPENED IMAGE\n",
      "-----------------------------------------\n",
      "££\n",
      "| just had a ton of energy all of\n",
      "a sudden and I’m like pacing\n",
      "back and forth in my room\n",
      "a\n",
      "\n",
      "-----------------------------------------\n",
      "TESSERACT OUTPUT --> CANNY EDGE IMAGE\n",
      "-----------------------------------------\n",
      "££\n",
      "| just had a ton of energy all of\n",
      "a sudden and I’m like pacing\n",
      "back and forth in my room\n",
      "a\n"
     ]
    }
   ],
   "source": [
    "custom_config = '--oem 3 --psm 6'\n",
    "print('-----------------------------------------')\n",
    "print('TESSERACT OUTPUT --> ORIGINAL IMAGE')\n",
    "print('-----------------------------------------')\n",
    "print(pytesseract.image_to_string(image, config=custom_config))\n",
    "print('\\n-----------------------------------------')\n",
    "print('TESSERACT OUTPUT --> THRESHOLDED IMAGE')\n",
    "print('-----------------------------------------')\n",
    "print(pytesseract.image_to_string(image, config=custom_config))\n",
    "print('\\n-----------------------------------------')\n",
    "print('TESSERACT OUTPUT --> OPENED IMAGE')\n",
    "print('-----------------------------------------')\n",
    "print(pytesseract.image_to_string(image, config=custom_config))\n",
    "print('\\n-----------------------------------------')\n",
    "print('TESSERACT OUTPUT --> CANNY EDGE IMAGE')\n",
    "print('-----------------------------------------')\n",
    "print(pytesseract.image_to_string(image, config=custom_config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "££\n",
      "| just had a ton of energy all of\n",
      "a sudden and I’m like pacing\n",
      "back and forth in my room\n",
      "a\n"
     ]
    }
   ],
   "source": [
    "text=pytesseract.image_to_string(image, config=custom_config)\n",
    "print(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "f= open(\"Outputs\\output 1.txt\",\"a+\") #change name as per the input.\n",
    "f.write(text)\n",
    "f.close()"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}