Handwriting recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2605ca83",
   "metadata": {},
   "source": [
    "# 1-Install NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e64a432",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\dell\\anaconda3\\lib\\site-packages (1.21.5)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install numpy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d65b9f1",
   "metadata": {},
   "source": [
    "# 2- Install cv2 (openCV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "311fb899",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencv-python in c:\\users\\dell\\anaconda3\\lib\\site-packages (4.6.0.66)\n",
      "Requirement already satisfied: numpy>=1.14.5 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from opencv-python) (1.21.5)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install opencv-python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68a20398",
   "metadata": {},
   "source": [
    "# 3- Install Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9859de43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: keras in c:\\users\\dell\\anaconda3\\lib\\site-packages (2.10.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install keras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c87dc6",
   "metadata": {},
   "source": [
    "# 4-Install Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3cc5a0da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in c:\\users\\dell\\anaconda3\\lib\\site-packages (2.10.0)\n",
      "Requirement already satisfied: gast<=0.4.0,>=0.2.1 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (0.4.0)\n",
      "Requirement already satisfied: flatbuffers>=2.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (22.10.26)\n",
      "Requirement already satisfied: keras-preprocessing>=1.1.1 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (1.1.2)\n",
      "Requirement already satisfied: tensorboard<2.11,>=2.10 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (2.10.1)\n",
      "Requirement already satisfied: setuptools in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (61.2.0)\n",
      "Note: you may need to restart the kernel to use updated packages.Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (1.42.0)\n",
      "Requirement already satisfied: six>=1.12.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (1.16.0)\n",
      "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (0.2.0)\n",
      "Requirement already satisfied: tensorflow-estimator<2.11,>=2.10.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (2.10.0)\n",
      "Requirement already satisfied: h5py>=2.9.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (3.6.0)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (3.3.0)\n",
      "\n",
      "Requirement already satisfied: keras<2.11,>=2.10.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (2.10.0)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (2.0.1)\n",
      "Requirement already satisfied: typing-extensions>=3.6.6 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (4.1.1)\n",
      "Requirement already satisfied: protobuf<3.20,>=3.9.2 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (3.19.1)\n",
      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (0.27.0)\n",
      "Requirement already satisfied: numpy>=1.20 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (1.21.5)\n",
      "Requirement already satisfied: packaging in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (21.3)\n",
      "Requirement already satisfied: libclang>=13.0.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (14.0.6)\n",
      "Requirement already satisfied: wrapt>=1.11.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (1.12.1)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (1.3.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorflow) (1.6.3)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from astunparse>=1.6.0->tensorflow) (0.37.1)\n",
      "Requirement already satisfied: google-auth<3,>=1.6.3 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (1.33.0)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (0.4.6)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (3.3.4)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (2.27.1)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (0.6.1)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (1.8.1)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from tensorboard<2.11,>=2.10->tensorflow) (2.0.3)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow) (4.2.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow) (0.2.8)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow) (4.7.2)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.11,>=2.10->tensorflow) (1.3.1)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.11,>=2.10->tensorflow) (0.4.8)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow) (1.26.9)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow) (2021.10.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow) (3.3)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.11,>=2.10->tensorflow) (2.0.4)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.11,>=2.10->tensorflow) (3.2.2)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from packaging->tensorflow) (3.0.4)\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9f6da55",
   "metadata": {},
   "source": [
    "# 5- Install matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af63971c",
   "metadata": {},
   "source": [
    "pip install matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4619b08",
   "metadata": {},
   "source": [
    "# 6- Install Pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "63438c9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: Pandas in c:\\users\\dell\\anaconda3\\lib\\site-packages (1.4.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from Pandas) (2.8.2)\n",
      "Requirement already satisfied: numpy>=1.18.5 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from Pandas) (1.21.5)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from Pandas) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.1->Pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install Pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fcfd2bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout\n",
    "from keras.optimizers import SGD, Adam\n",
    "from keras.callbacks import ReduceLROnPlateau, EarlyStopping\n",
    "from keras.utils import to_categorical\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9bde2a5",
   "metadata": {},
   "source": [
    "# Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "965b3372",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     0  0.1  0.2  0.3  0.4  0.5  0.6  0.7  0.8  0.9  ...  0.639  0.640  0.641  \\\n",
      "0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0    0.0   \n",
      "1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0    0.0   \n",
      "2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0    0.0   \n",
      "3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0    0.0   \n",
      "4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0    0.0   \n",
      "5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0    0.0   \n",
      "6  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0    0.0   \n",
      "7  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0    0.0   \n",
      "8  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0    0.0   \n",
      "9  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0    0.0    0.0   \n",
      "\n",
      "   0.642  0.643  0.644  0.645  0.646  0.647  0.648  \n",
      "0    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
      "1    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
      "2    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
      "3    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
      "4    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
      "5    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
      "6    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
      "7    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
      "8    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
      "9    0.0    0.0    0.0    0.0    0.0    0.0    0.0  \n",
      "\n",
      "[10 rows x 785 columns]\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"A_Z Handwritten Data.csv\").astype('float32')\n",
    "\n",
    "print(data.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8dd704f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop('0', axis =1)\n",
    "y = data['0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2eba25de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (297960, 28, 28)\n",
      "Test data shape:  (74490, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(x, y, test_size =0.2)\n",
    "\n",
    "train_x = np.reshape(train_x.values, (train_x.shape[0],28,28))\n",
    "test_x = np.reshape(test_x.values, (test_x.shape[0],28,28))\n",
    "\n",
    "print(\"Train data shape: \", train_x.shape)\n",
    "print(\"Test data shape: \", test_x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b72c125e",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_dict = {0:'A',1:'B',2:'C',3:'D',4:'E',5:'F',6:'G',7:'H',8:'I',9:'J',10:'K',11:'L',12:'M',13:'N',14:'O',15:'P',16:'Q',17:'R',18:'S',19:'T',20:'U',21:'V',22:'W',23:'X', 24:'Y',25:'Z'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f738fbb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_int = np.int0(y)\n",
    "count = np.zeros(26,dtype='int')\n",
    "for i in y_int:\n",
    "    count[i] +=1\n",
    "    \n",
    "alphabets = []\n",
    "for i in word_dict.values():\n",
    "    alphabets.append(i)\n",
    "    \n",
    " \n",
    "fig, ax = plt.subplots(1,1, figsize=(10,10))\n",
    "ax.barh(alphabets,count)\n",
    "\n",
    "plt.xlabel(\"Number of elements\")\n",
    "plt.ylabel(\"Alphabets\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7b7a7b81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "shuff = shuffle(train_x[:100])\n",
    "\n",
    "fig, ax = plt.subplots(3,3, figsize = (10,10))\n",
    "axes = ax.flatten()\n",
    "\n",
    "for i in range(9):\n",
    "    _,shu = cv2.threshold(shuff[i],30,200, cv2.THRESH_BINARY)\n",
    "    axes[i].imshow(np.reshape(shuff[i], (28,28)), cmap=\"Greys\")\n",
    "plt.show()    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd873540",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}