OCR_MNIST.ipynb

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  "metadata": {
    "colab": {
      "name": "OCR MNIST.ipynb",
      "provenance": [],
      "toc_visible": true,
      "mount_file_id": "https://github.com/amoghjalan/Optical-Character-Recognition/blob/main/OCR_MNIST.ipynb",
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      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/amoghjalan/Optical-Character-Recognition/blob/main/OCR_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dp6ozUb42MTC"
      },
      "source": [
        "# Optical Character Recognition\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-Lq_cSS2jSr"
      },
      "source": [
        "# Importing necessory Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import os\n",
        "import imutils"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LOkGY9667zU4"
      },
      "source": [
        "from keras.utils import np_utils\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.layers import Dense, Flatten, Dropout, BatchNormalization\n",
        "from keras import regularizers, optimizers\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "\n",
        "from keras import backend as K"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_HbFXh82VRJ"
      },
      "source": [
        "##Loading MNIST Dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3s_kldfJzFl2",
        "outputId": "bcf4b8d2-65bc-4dcc-b91c-0ef1d7d3bca8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Dataset unzipped from Drive\n",
        "! unzip \"/content/drive/My Drive/Dataset/OCR/train.csv.zip\""
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/My Drive/Dataset/OCR/train.csv.zip\n",
            "  inflating: train.csv               \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_1pX7r_3GD7"
      },
      "source": [
        "#Loading training data in train_df\n",
        "train_df = pd.read_csv(\"train.csv\")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KEuX8YKY3Qop",
        "outputId": "a06f4e9c-6ce9-4bdc-902b-cb6423125fb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  pixel0  pixel1  pixel2  ...  pixel780  pixel781  pixel782  pixel783\n",
              "0      1       0       0       0  ...         0         0         0         0\n",
              "1      0       0       0       0  ...         0         0         0         0\n",
              "2      1       0       0       0  ...         0         0         0         0\n",
              "3      4       0       0       0  ...         0         0         0         0\n",
              "4      0       0       0       0  ...         0         0         0         0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FDu4xant3l0N",
        "outputId": "f8221db5-10f6-4a3a-9f71-fe7529fa1134",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data = train_df.values    # Converting the data from dataframe to a numpy array\n",
        "\n",
        "X = data[:,1:]            # X has all the pixel values i.e from column 1 to last column\n",
        "Y = data[:,0]             # Y has the labels of all the images \n",
        "\n",
        "print(X.shape,Y.shape)  "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(42000, 784) (42000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "henxLamk61So",
        "outputId": "cecf9668-0904-4638-d692-1c41d0ace1cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "split = int(0.8*X.shape[0])                                # Splitting the data in 80:20 ratio. 80 being train, 20 being test(validation)\n",
        "\n",
        "X_train = X[:split,:]                                      # Splitted X and converted the type of values to float of 32 bit\n",
        "Y_train = Y[:split]                                        # Splitted Y\n",
        "\n",
        "X_test = X[split:,:]                                       # Splitted X and converted the type of values to float of 32 bit\n",
        "Y_test = Y[split:]                                         # Splitted Y\n",
        "\n",
        "X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)     # Reshaped the image data from (784) to (28,28,1)\n",
        "X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)        # Reshaped the image data from (784) to (28,28,1)\n",
        "\n",
        "print(X_train.shape,Y_train.shape)\n",
        "print(X_test.shape,Y_test.shape)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(33600, 28, 28, 1) (33600,)\n",
            "(8400, 28, 28, 1) (8400,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZ7zXeWUj9cn"
      },
      "source": [
        "X_train1 = X_train[:16000,:]\n",
        "X_train2 = 255-X_train[16000:33600,:]\n",
        "X_train = np.concatenate((X_train1, X_train2)).astype(np.float32)\n",
        "\n",
        "X_test1 = X_test[:4200,:]\n",
        "X_test2 = 255-X_test[4200:33600,:]\n",
        "X_test = np.concatenate((X_test1, X_test2)).astype(np.float32)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83S6ohE27dPF"
      },
      "source": [
        "X_train = X_train/255                                      # Standardized the pixel values\n",
        "X_test = X_test/255                                        # Standardized the pixel values\n",
        "\n",
        "Y_train = np_utils.to_categorical(Y_train, 10)             # Converted the labels as categorical variables\n",
        "Y_test = np_utils.to_categorical(Y_test, 10)               # Converted the labels as categorical variables"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLBgidJU7uTm",
        "outputId": "5fb7424c-af84-4e10-b9b0-5e244c748201",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# Visualizing some images from the data\n",
        "def drawImg(sample):\n",
        "    img = sample.reshape((28,28))\n",
        "    plt.imshow(img,cmap='gray')    \n",
        "    plt.show()  \n",
        "\n",
        "drawImg(X_test[4201])  "
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6TGH4SI7-hXZ"
      },
      "source": [
        "## Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMyv4xZs-jgN",
        "outputId": "e5977e12-4e25-423e-e597-a4fd60b7a137",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        }
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu', input_shape = (28,28,1)))\n",
        "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same', activation ='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu'))\n",
        "model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', activation ='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation = \"relu\"))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(10, activation = \"softmax\"))\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 28, 28, 32)        832       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 28, 28, 32)        25632     \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 14, 14, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 14, 14, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 3136)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               803072    \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                2570      \n",
            "=================================================================\n",
            "Total params: 887,530\n",
            "Trainable params: 887,530\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Y4cVClNHjYL",
        "outputId": "bdc57a0d-3812-4813-9ffb-481bd501d540",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "checkpoint=ModelCheckpoint('/content/drive/My Drive/Dataset/OCR/MNISTmodel_weights_new_final.h5',monitor='val_loss',verbose=0,save_best_only=True,save_weights_only=True,mode='auto',period=1)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqnY07k4Ir0m",
        "outputId": "3af839f3-4f81-4e73-a0c0-a5a17da3eb88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        }
      },
      "source": [
        "model.fit(X_train, Y_train, validation_data=(X_test, Y_test), epochs=15, batch_size=200, callbacks=[checkpoint])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "  1/168 [..............................] - ETA: 0s - loss: 2.3184 - accuracy: 0.0800WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0049s vs `on_train_batch_end` time: 0.0125s). Check your callbacks.\n",
            "168/168 [==============================] - 2s 14ms/step - loss: 0.6368 - accuracy: 0.7797 - val_loss: 0.1036 - val_accuracy: 0.9661\n",
            "Epoch 2/15\n",
            "168/168 [==============================] - 2s 13ms/step - loss: 0.1408 - accuracy: 0.9579 - val_loss: 0.0789 - val_accuracy: 0.9740\n",
            "Epoch 3/15\n",
            "168/168 [==============================] - 2s 13ms/step - loss: 0.0955 - accuracy: 0.9717 - val_loss: 0.0471 - val_accuracy: 0.9843\n",
            "Epoch 4/15\n",
            "168/168 [==============================] - 2s 14ms/step - loss: 0.0737 - accuracy: 0.9772 - val_loss: 0.0398 - val_accuracy: 0.9874\n",
            "Epoch 5/15\n",
            "168/168 [==============================] - 2s 14ms/step - loss: 0.0612 - accuracy: 0.9803 - val_loss: 0.0427 - val_accuracy: 0.9857\n",
            "Epoch 6/15\n",
            "168/168 [==============================] - 2s 14ms/step - loss: 0.0534 - accuracy: 0.9841 - val_loss: 0.0382 - val_accuracy: 0.9877\n",
            "Epoch 7/15\n",
            "168/168 [==============================] - 2s 14ms/step - loss: 0.0505 - accuracy: 0.9844 - val_loss: 0.0364 - val_accuracy: 0.9892\n",
            "Epoch 8/15\n",
            "168/168 [==============================] - 2s 14ms/step - loss: 0.0447 - accuracy: 0.9863 - val_loss: 0.0355 - val_accuracy: 0.9894\n",
            "Epoch 9/15\n",
            "168/168 [==============================] - 2s 13ms/step - loss: 0.0422 - accuracy: 0.9871 - val_loss: 0.0338 - val_accuracy: 0.9898\n",
            "Epoch 10/15\n",
            "168/168 [==============================] - 2s 13ms/step - loss: 0.0384 - accuracy: 0.9882 - val_loss: 0.0374 - val_accuracy: 0.9894\n",
            "Epoch 11/15\n",
            "168/168 [==============================] - 2s 13ms/step - loss: 0.0337 - accuracy: 0.9893 - val_loss: 0.0425 - val_accuracy: 0.9883\n",
            "Epoch 12/15\n",
            "168/168 [==============================] - 2s 14ms/step - loss: 0.0339 - accuracy: 0.9894 - val_loss: 0.0314 - val_accuracy: 0.9912\n",
            "Epoch 13/15\n",
            "168/168 [==============================] - 2s 13ms/step - loss: 0.0298 - accuracy: 0.9896 - val_loss: 0.0311 - val_accuracy: 0.9925\n",
            "Epoch 14/15\n",
            "168/168 [==============================] - 2s 14ms/step - loss: 0.0303 - accuracy: 0.9904 - val_loss: 0.0297 - val_accuracy: 0.9914\n",
            "Epoch 15/15\n",
            "168/168 [==============================] - 2s 13ms/step - loss: 0.0272 - accuracy: 0.9915 - val_loss: 0.0311 - val_accuracy: 0.9907\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ff9ac90fe10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "79IhHW7pljrm"
      },
      "source": [
        "## Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmf_39kAllAg"
      },
      "source": [
        "model.load_weights(\"/content/drive/My Drive/Dataset/OCR/MNISTmodel_weights_new_final.h5\")     # Loaded best model Weights"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWgyvX2Tl8O7",
        "outputId": "0a36bcd1-0342-4bb6-d820-997a6376df61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "! unzip \"/content/drive/My Drive/Dataset/OCR/test.csv.zip\"                          # Unzipped test data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/drive/My Drive/Dataset/OCR/test.csv.zip\n",
            "  inflating: test.csv                \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zFcv59JmFMd"
      },
      "source": [
        "test_df = pd.read_csv(\"test.csv\")                                                   # Loaded test data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHsyyKrSmN0a"
      },
      "source": [
        "test_data = test_df.values                                                          # Converted dataframe into numpy array"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1nfRSMwMm3Az"
      },
      "source": [
        "# Prediction function\n",
        "def predict_number(img):\n",
        "  pred_img = np.resize(img,(1,28,28,1))                # Resixe the pixels from 784 to (1,28,28,1)\n",
        "  pred = model.predict(pred_img)                       # Predicted the label \n",
        "  pred = pred.argmax()                                 # Converted predicted value from categorical variable to a number\n",
        "  img = img.reshape((28,28))                           # For plotting the image\n",
        "  plt.imshow(img,cmap='gray')    \n",
        "  plt.show()\n",
        "  print(pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jG_pYN8Rnt7Y",
        "outputId": "a3096e31-cc20-4832-f09b-bdd13f91ba6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "predict_number(test_data[66])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qdYnp2JSn1wX",
        "outputId": "245f5f88-81e8-4c29-80ab-f8349975411f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        }
      },
      "source": [
        "X= data[:, 1:]\n",
        "Y= data[:, 0]\n",
        "def accuracy(data, model):\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-37-ce0afbe5fc9d>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    \u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eucAc8g9xmiE",
        "outputId": "097b8f88-e4f5-40fd-9d16-af453080da1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_test.shape[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8400"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "raYFMSNLxg0R",
        "outputId": "d239728c-4b66-4770-f479-6a2ce7e6a6f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Accuracy predictor\n",
        "correct=0\n",
        "\n",
        "for i in range(8400):\n",
        "  img = X_test[i].reshape(1,28,28,1)\n",
        "  pred = model.predict(img)\n",
        "  pred = pred.argmax()\n",
        "  if Y_test[i].argmax() == pred:\n",
        "    correct += 1\n",
        "\n",
        "accuracy = (correct/X_test.shape[0])*100\n",
        "print(f\"Accuracy={accuracy}\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy=99.28571428571429\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "urV8lQ8I3sbB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2vahrbqmiFT"
      },
      "source": [
        "Prediction File"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBHiCHE3q60I"
      },
      "source": [
        "model.load_weights(\"/content/drive/My Drive/Dataset/OCR/MNISTmodel_weights_new_final.h5\")     # Loaded best model Weights"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Um0vczy9wQ_I"
      },
      "source": [
        "def load_images(folder):\n",
        "    img_names = []\n",
        "    for filename in os.listdir(folder):\n",
        "      img_names.append(filename)\n",
        "    sorted(img_names)\n",
        "    images = []\n",
        "    for img_name in img_names:\n",
        "      img = cv2.imread(os.path.join(folder, img_name))\n",
        "      img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "      img = imutils.resize(img, width=28, height=28)\n",
        "      img = cv2.resize(img, (28,28), interpolation = cv2.INTER_AREA)\n",
        "      img = (img.astype(np.float32))/255\n",
        "      images.append(img)\n",
        "    return np.array(images)  \n"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AFKVxnrtnEnp"
      },
      "source": [
        "images = load_images(\"/content/drive/My Drive/Dataset/OCR/test_set\")"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4nmpbt9Pat50",
        "outputId": "4b56818b-6160-4a34-8eb1-28f3c84814c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "images.shape"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "prDgi1CtbpgW",
        "outputId": "b7e4953f-ecb2-4dc9-8e98-7b5fa1fc35a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "images[0].shape"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6yWp6RhdJe7",
        "outputId": "f26b032f-81ee-49ac-961d-5c0881171d72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "\n",
        "drawImg(images[6])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mhH2GpH3Dvt+v1lrv3SX92cy2m9nqZjczgqnu3p/d/0DS1GY2M4LCZbwb6aRlxlvmuatl+fOyeIPu713q7t+XtFjSmuzlakvyob/BWmnudFTLeDfKCMuM/00zn7talz8vqxlhPyBp5rDvv5NtawnufiC7PSTpCbXeUtQHj6+gm90eanI/f9NKy3iPtMy4WuC5a+by580Ie4+kC8xslpmNlbRMUnqp0AYxs7OyN05kZmdJ+qFabynqzZJWZvdXSnqyib2coFWW8c5bZlxNfu6avvy5uzf8S9ISDb0j/3+S7mpGDzl9nSfpf7Ov3c3uTdKjGnpZ95WG3tu4WdI/SNoq6R1J/yOpvYV6+09JOyW9rqFgTW9Sb5dq6CX665J2ZF9Lmv3cJfpqyPPGx2WBIHiDDgiCsANBEHYgCMIOBEHYgSAIOxAEYQeC+H+yP7ev3fyi5QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OzLrRqnDerVw"
      },
      "source": [
        "\n",
        "with open(\"output.txt\", 'a+') as f:\n",
        "    for i in range(images.shape[0]):\n",
        "      img = images[i].reshape(1,28,28,1)\n",
        "      pred = model.predict(img)\n",
        "      pred = pred.argmax()\n",
        "      f.write(str(pred)+\"\\n\")\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "99RHJGAwjPQE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}