character_prediction_cnn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMHDKPuTjdijutq5z8mSBry",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Phanindhra-Reddy/Machine-Learning/blob/main/character_prediction_cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! gdown --id 1eFy9nrQ_Mb1-z9fiYzobZAJCWQVzKVvG"
      ],
      "metadata": {
        "id": "SH98v8JsBgDw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae25af14-f66b-43e0-bdb2-98fdd67d7cb0"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gdown/cli.py:131: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  category=FutureWarning,\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1eFy9nrQ_Mb1-z9fiYzobZAJCWQVzKVvG\n",
            "To: /content/A_Z Handwritten Data.csv\n",
            "100% 699M/699M [00:06<00:00, 113MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Hnb_S8tMjn7A"
      },
      "outputs": [],
      "source": [
        "# Loading all the necessary libraries\n",
        "import cv2\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils import shuffle\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"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the MNIST dataset and convert the type using pandas.\n",
        "data = pd.read_csv(r\"A_Z Handwritten Data.csv\").astype('float32')"
      ],
      "metadata": {
        "id": "_AkJca5tkBmi"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split and Reshape the data\n",
        "X = data.drop('0',axis = 1)\n",
        "y = data['0']\n",
        "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",
        "# converting floating point value to integer value of the labels\n",
        "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'}"
      ],
      "metadata": {
        "id": "pcJzdK6alNLt"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the number of alphabets in the dataset\n",
        "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",
        "fig, ax = plt.subplots(1,1, figsize=(10,10))\n",
        "ax.barh(alphabets, count)\n",
        "\n",
        "# naming the x axis\n",
        "plt.xlabel(\"Number of elements\")\n",
        "# naming the y axis\n",
        "plt.ylabel(\"Alphabets\")\n",
        "# giving a title  \n",
        "plt.title(\"Plotting the number of alphabets\")\n",
        "# Turn on the minor TICKS, which are required for the minor GRID\n",
        "plt.minorticks_on()\n",
        "# Customize the major grid\n",
        "plt.grid(which='major', linestyle='-', linewidth='0.5', color='red')\n",
        "# Customize the minor grid\n",
        "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "f_57UCIulb0r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "7134419b-06f1-4e30-c347-6784211870fe"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Shuffling the data\n",
        "shuff = shuffle(train_x[:100])\n",
        "fig, ax = plt.subplots(3,3, figsize = (10,10))\n",
        "axes = ax.flatten()\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=plt.get_cmap('gray'))\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "u0tH3KTvlro0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "outputId": "1266e992-d8d4-40f7-e853-bbdaa90ac8b5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reshape data for model creation\n",
        "train_X = train_x.reshape(train_x.shape[0],train_x.shape[1],train_x.shape[2],1)\n",
        "print(\"The new shape of train data: \", train_X.shape)\n",
        "\n",
        "test_X = test_x.reshape(test_x.shape[0], test_x.shape[1], test_x.shape[2],1)\n",
        "print(\"The new shape of train data: \", test_X.shape)\n",
        "\n",
        "train_yOHE = to_categorical(train_y, num_classes = 26, dtype='int')\n",
        "print(\"The new shape of train labels: \", train_yOHE.shape)\n",
        "\n",
        "test_yOHE = to_categorical(test_y, num_classes = 26, dtype='int')\n",
        "print(\"The new shape of test labels: \", test_yOHE.shape)\n"
      ],
      "metadata": {
        "id": "gHviH-Rglz3b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1dc78b3-8fe9-4096-82f7-81b9af5f57c0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The new shape of train data:  (297960, 28, 28, 1)\n",
            "The new shape of train data:  (74490, 28, 28, 1)\n",
            "The new shape of train labels:  (297960, 26)\n",
            "The new shape of test labels:  (74490, 26)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model creation\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(28,28,1)))\n",
        "model.add(MaxPool2D(pool_size=(2, 2), strides=2))\n",
        "\n",
        "model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding = 'same'))\n",
        "model.add(MaxPool2D(pool_size=(2, 2), strides=2))\n",
        "\n",
        "model.add(Conv2D(filters=128, kernel_size=(3, 3), activation='relu', padding = 'valid'))\n",
        "model.add(MaxPool2D(pool_size=(2, 2), strides=2))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(64,activation =\"relu\"))\n",
        "model.add(Dense(128,activation =\"relu\"))\n",
        "\n",
        "model.add(Dense(26,activation =\"softmax\"))\n",
        "# compiling and fitting model\n",
        "model.compile(optimizer = Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_X, train_yOHE, epochs=1,  validation_data = (test_X,test_yOHE))\n",
        "\n",
        "# save model\n",
        "model.summary()\n",
        "model.save(r'model_hand.h5')\n",
        "print(\"The validation accuracy is :\", history.history['val_accuracy'])\n",
        "print(\"The training accuracy is :\", history.history['accuracy'])\n",
        "print(\"The validation loss is :\", history.history['val_loss'])\n",
        "print(\"The training loss is :\", history.history['loss'])"
      ],
      "metadata": {
        "id": "_-DfXEZAl27N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5a4114d-0478-41ed-a4bf-2d0b9f7581b9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9312/9312 [==============================] - 456s 49ms/step - loss: 0.1622 - accuracy: 0.9544 - val_loss: 0.0791 - val_accuracy: 0.9791\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 6, 6, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 4, 4, 128)         73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 2, 2, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                32832     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               8320      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 26)                3354      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 137,178\n",
            "Trainable params: 137,178\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "The validation accuracy is : [0.9790844321250916]\n",
            "The training accuracy is : [0.9544469118118286]\n",
            "The validation loss is : [0.07909820973873138]\n",
            "The training loss is : [0.16220088303089142]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "model = load_model('model_hand.h5')\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "2hlJqEJ0mGKr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "098ece68-779f-435e-b255-5c5f13a662aa"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 6, 6, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 4, 4, 128)         73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 2, 2, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                32832     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               8320      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 26)                3354      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 137,178\n",
            "Trainable params: 137,178\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction on test data\n",
        "fig, axes = plt.subplots(3,3, figsize=(8,9))\n",
        "axes = axes.flatten()\n",
        "\n",
        "for i,ax in enumerate(axes):\n",
        "    img = np.reshape(test_X[i], (28,28))\n",
        "    ax.imshow(img,  cmap=plt.get_cmap('gray'))\n",
        "    \n",
        "    pred = word_dict[np.argmax(test_yOHE[i])]\n",
        "    ax.set_title(\"Prediction: \"+pred)"
      ],
      "metadata": {
        "id": "UcC4pHgsmHeB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "821e793d-ccdc-4674-fcd4-771bc6ec282f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x648 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab.patches import cv2_imshow"
      ],
      "metadata": {
        "id": "QPROh3XRaer0"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predection on External Image\n",
        "\n",
        "img = cv2.imread(r'a1.png')\n",
        "img_copy = img.copy()\n",
        "\n",
        "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "img = cv2.resize(img, (400,440))\n",
        "\n",
        "\n",
        "img_copy = cv2.GaussianBlur(img_copy, (7,7), 0)\n",
        "img_gray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)\n",
        "_, img_thresh = cv2.threshold(img_gray, 100, 255, cv2.THRESH_BINARY_INV)\n",
        "\n",
        "img_final = cv2.resize(img_thresh, (28,28))\n",
        "img_final =np.reshape(img_final, (1,28,28,1))\n",
        "img_pred = word_dict[np.argmax(model.predict(img_final))]\n",
        "\n",
        "cv2.putText(img, \"Image Data\", (100,25), cv2.FONT_HERSHEY_DUPLEX , fontScale= 1, thickness=2, color = (255,0,0))\n",
        "cv2.putText(img, \"Character Prediction: \" + img_pred, (10,410), cv2.FONT_HERSHEY_SIMPLEX, fontScale= 1, thickness=2, color = (0,0,255))\n",
        "cv2_imshow(img)"
      ],
      "metadata": {
        "id": "Z3UfNU2smJ8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "outputId": "fee092a1-c32b-4cb1-8831-956468a3bc62"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 80ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=400x440 at 0x7FA4A5B26290>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}