OCRDemo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the libraries and dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pylab as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.gray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pixel of gray scale image. values range from 0 to 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2083a1107f0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(digits.images[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  7.,  8., 13., 16., 15.,  1.],\n",
       "       [ 0.,  0.,  7.,  7.,  4., 11., 12.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  8., 13.,  1.,  0.],\n",
       "       [ 0.,  4.,  8.,  8., 15., 15.,  6.,  0.],\n",
       "       [ 0.,  2., 11., 15., 15.,  4.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., 16.,  5.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  9., 15.,  1.,  0.,  0.,  0.],\n",
       "       [ 0.,  0., 13.,  5.,  0.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.images[7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import ensemble "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_sample = len(digits.images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1797"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = digits.images.reshape(n_sample,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0., 12., 10.,  0.,  0.,  0.,  0.,  0.,  0., 14., 16., 16.,\n",
       "       14.,  0.,  0.,  0.,  0., 13., 16., 15., 10.,  1.,  0.,  0.,  0.,\n",
       "       11., 16., 16.,  7.,  0.,  0.,  0.,  0.,  0.,  4.,  7., 16.,  7.,\n",
       "        0.,  0.,  0.,  0.,  0.,  4., 16.,  9.,  0.,  0.,  0.,  5.,  4.,\n",
       "       12., 16.,  4.,  0.,  0.,  0.,  9., 16., 16., 10.,  0.,  0.])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create random indices \n",
    "sample_index = random.sample(range(len(x)),int(len(x)/5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 1312,\n",
       " 924,\n",
       " 596,\n",
       " 206,\n",
       " 649,\n",
       " 29,\n",
       " 303,\n",
       " 1274,\n",
       " 25,\n",
       " 385,\n",
       " 866,\n",
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       " 632,\n",
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       " 1527,\n",
       " 1635,\n",
       " 150,\n",
       " 369,\n",
       " 499,\n",
       " 1484,\n",
       " 123,\n",
       " 1630,\n",
       " 45,\n",
       " 1743,\n",
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       " 550,\n",
       " 1568,\n",
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       " 600,\n",
       " 833,\n",
       " 60,\n",
       " 58,\n",
       " 4,\n",
       " 1618,\n",
       " 1308,\n",
       " 172,\n",
       " 1080]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_index = [i for i in range(len(x)) if i not in sample_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 1135,\n",
       " 1136,\n",
       " 1137,\n",
       " 1139,\n",
       " 1140,\n",
       " 1141,\n",
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       " 1150,\n",
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       " 1155,\n",
       " 1156,\n",
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       " 1159,\n",
       " 1160,\n",
       " 1161,\n",
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       " 1165,\n",
       " 1167,\n",
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       " 1170,\n",
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       " 1190,\n",
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       " 1215,\n",
       " 1216,\n",
       " 1217,\n",
       " 1218,\n",
       " 1219,\n",
       " 1220,\n",
       " 1221,\n",
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       " 1224,\n",
       " 1225,\n",
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       " 1228,\n",
       " 1229,\n",
       " 1230,\n",
       " 1231,\n",
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       " 1234,\n",
       " 1236,\n",
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       " 1239,\n",
       " 1241,\n",
       " 1242,\n",
       " 1243,\n",
       " 1244,\n",
       " 1246,\n",
       " ...]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_images=[x[i] for i in sample_index]\n",
    "valid_images=[x[i] for i in valid_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_target=[y[i] for i in sample_index]\n",
    "valid_target=[y[i] for i in valid_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = ensemble.RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Meher\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(sample_images, sample_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Tree Classifier:\n",
      "\n",
      "Score\t 0.894297635605007\n"
     ]
    }
   ],
   "source": [
    "score = classifier.score(valid_images, valid_target)\n",
    "print ('Random Tree Classifier:\\n') \n",
    "print ('Score\\t',str(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([7])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i=300\n",
    "\n",
    "pl.gray() \n",
    "pl.matshow(digits.images[i]) \n",
    "pl.show() \n",
    "classifier.predict(x[[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pillow in c:\\users\\meher\\anaconda3\\lib\\site-packages (6.0.0)\n",
      "Requirement already satisfied: pytesseract in c:\\users\\meher\\anaconda3\\lib\\site-packages (0.2.7)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pillow pytesseract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytesseract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = Image.open(\"download.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pytesseract in c:\\users\\meher\\anaconda3\\lib\\site-packages (0.2.7)\n",
      "Requirement already satisfied: Pillow in c:\\users\\meher\\anaconda3\\lib\\site-packages (from pytesseract) (6.0.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pytesseract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pytesseract.pytesseract.tesseract_cmd = r'C:\\Program Files\\Tesseract-OCR\\tesseract.exe'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = pytesseract.image_to_string(im,lang = 'eng')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Don't Do This!\n",
      "\n",
      "Heret am writing down every word that am going to\n",
      "say aloud when gwe the presentation. Allthe tex\n",
      "taking up spaceon the sreen going to make really\n",
      "ctfcut for people to listen and to read everything at\n",
      "fence, even though they ae the same thing Ra be\n",
      "better just want and added a coupe key points on\n",
      "the sik and elaborate on them by referring tosome\n",
      "ash eares or memoriaing what Im going 0535.\n"
     ]
    }
   ],
   "source": [
    "print (text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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