Handwriting_recognition.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4w0iMZ9gSY4"
      },
      "source": [
        "## Data collection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "paQEamXogSY6"
      },
      "outputs": [],
      "source": [
        "!wget -q https://git.io/J0fjL -O IAM_Words.zip\n",
        "!unzip -qq IAM_Words.zip\n",
        "!\n",
        "!mkdir data\n",
        "!mkdir data/words\n",
        "!tar -xf IAM_Words/words.tgz -C data/words\n",
        "!mv IAM_Words/words.txt data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gROp7E-VgSZD"
      },
      "source": [
        "Preview how the dataset is organized. Lines prepended by \"#\" are just metadata information."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "-RUotVX3gSZF",
        "outputId": "8673bd24-c5b4-4864-e4b0-a615be0791fd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#--- words.txt ---------------------------------------------------------------#\n",
            "#\n",
            "# iam database word information\n",
            "#\n",
            "# format: a01-000u-00-00 ok 154 1 408 768 27 51 AT A\n",
            "#\n",
            "#     a01-000u-00-00  -> word id for line 00 in form a01-000u\n",
            "#     ok              -> result of word segmentation\n",
            "#                            ok: word was correctly\n",
            "#                            er: segmentation of word can be bad\n",
            "#\n",
            "#     154             -> graylevel to binarize the line containing this word\n",
            "#     1               -> number of components for this word\n",
            "#     408 768 27 51   -> bounding box around this word in x,y,w,h format\n",
            "#     AT              -> the grammatical tag for this word, see the\n",
            "#                        file tagset.txt for an explanation\n",
            "#     A               -> the transcription for this word\n",
            "#\n",
            "a01-000u-00-00 ok 154 408 768 27 51 AT A\n",
            "a01-000u-00-01 ok 154 507 766 213 48 NN MOVE\n"
          ]
        }
      ],
      "source": [
        "!head -20 data/words.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0mCPYz7egSZG"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "kumuLrlNgSZI"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers.experimental.preprocessing import StringLookup\n",
        "from tensorflow import keras\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "np.random.seed(42)\n",
        "tf.random.set_seed(42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UYhubi-WgSZK"
      },
      "source": [
        "## Dataset splitting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "3gzgKpdegSZM"
      },
      "outputs": [],
      "source": [
        "base_path = \"data\"\n",
        "words_list = []\n",
        "\n",
        "words = open(f\"{base_path}/words.txt\", \"r\").readlines()\n",
        "for line in words:\n",
        "    if line[0] == \"#\":\n",
        "        continue\n",
        "    if line.split(\" \")[1] != \"err\":  # We don't need to deal with errored entries.\n",
        "        words_list.append(line)\n",
        "\n",
        "len(words_list)\n",
        "\n",
        "np.random.shuffle(words_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "NS4W1C9lgSZU",
        "outputId": "487e7ad8-bb15-4ad1-c5ba-254757986982",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total training samples: 86810\n",
            "Total validation samples: 4823\n",
            "Total test samples: 4823\n"
          ]
        }
      ],
      "source": [
        "split_idx = int(0.9 * len(words_list))\n",
        "train_samples = words_list[:split_idx]\n",
        "test_samples = words_list[split_idx:]\n",
        "\n",
        "val_split_idx = int(0.5 * len(test_samples))\n",
        "validation_samples = test_samples[:val_split_idx]\n",
        "test_samples = test_samples[val_split_idx:]\n",
        "\n",
        "assert len(words_list) == len(train_samples) + len(validation_samples) + len(\n",
        "    test_samples\n",
        ")\n",
        "\n",
        "print(f\"Total training samples: {len(train_samples)}\")\n",
        "print(f\"Total validation samples: {len(validation_samples)}\")\n",
        "print(f\"Total test samples: {len(test_samples)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "lgORngHqgSZY"
      },
      "outputs": [],
      "source": [
        "base_image_path = os.path.join(base_path, \"words\")\n",
        "\n",
        "\n",
        "def get_image_paths_and_labels(samples):\n",
        "    paths = []\n",
        "    corrected_samples = []\n",
        "    for (i, file_line) in enumerate(samples):\n",
        "        line_split = file_line.strip()\n",
        "        line_split = line_split.split(\" \")\n",
        "\n",
        "        # Each line split will have this format for the corresponding image:\n",
        "        # part1/part1-part2/part1-part2-part3.png\n",
        "        image_name = line_split[0]\n",
        "        partI = image_name.split(\"-\")[0]\n",
        "        partII = image_name.split(\"-\")[1]\n",
        "        img_path = os.path.join(\n",
        "            base_image_path, partI, partI + \"-\" + partII, image_name + \".png\"\n",
        "        )\n",
        "        if os.path.getsize(img_path):\n",
        "            paths.append(img_path)\n",
        "            corrected_samples.append(file_line.split(\"\\n\")[0])\n",
        "\n",
        "    return paths, corrected_samples\n",
        "\n",
        "\n",
        "train_img_paths, train_labels = get_image_paths_and_labels(train_samples)\n",
        "validation_img_paths, validation_labels = get_image_paths_and_labels(validation_samples)\n",
        "test_img_paths, test_labels = get_image_paths_and_labels(test_samples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZKkrczXYgSZf"
      },
      "source": [
        "Then we prepare the ground-truth labels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "x9qea0yVgSZi",
        "outputId": "5b9268a8-2c7a-494b-c165-7c0216e376e3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum length:  21\n",
            "Vocab size:  78\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sure',\n",
              " 'he',\n",
              " 'during',\n",
              " 'of',\n",
              " 'booty',\n",
              " 'gastronomy',\n",
              " 'boy',\n",
              " 'The',\n",
              " 'and',\n",
              " 'in']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Find maximum length and the size of the vocabulary in the training data.\n",
        "train_labels_cleaned = []\n",
        "characters = set()\n",
        "max_len = 0\n",
        "\n",
        "for label in train_labels:\n",
        "    label = label.split(\" \")[-1].strip()\n",
        "    for char in label:\n",
        "        characters.add(char)\n",
        "\n",
        "    max_len = max(max_len, len(label))\n",
        "    train_labels_cleaned.append(label)\n",
        "\n",
        "characters = sorted(list(characters))\n",
        "\n",
        "print(\"Maximum length: \", max_len)\n",
        "print(\"Vocab size: \", len(characters))\n",
        "\n",
        "# Check some label samples.\n",
        "train_labels_cleaned[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iF0uj85GgSZk"
      },
      "source": [
        "Now we clean the validation and the test labels as well."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "45Scq3FYgSZo"
      },
      "outputs": [],
      "source": [
        "\n",
        "def clean_labels(labels):\n",
        "    cleaned_labels = []\n",
        "    for label in labels:\n",
        "        label = label.split(\" \")[-1].strip()\n",
        "        cleaned_labels.append(label)\n",
        "    return cleaned_labels\n",
        "\n",
        "\n",
        "validation_labels_cleaned = clean_labels(validation_labels)\n",
        "test_labels_cleaned = clean_labels(test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "3htzt8aQgSZr"
      },
      "outputs": [],
      "source": [
        "\n",
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "\n",
        "# Mapping characters to integers.\n",
        "char_to_num = StringLookup(vocabulary=list(characters), mask_token=None)\n",
        "\n",
        "# Mapping integers back to original characters.\n",
        "num_to_char = StringLookup(\n",
        "    vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "8C5h9P2JgSZv"
      },
      "outputs": [],
      "source": [
        "\n",
        "def distortion_free_resize(image, img_size):\n",
        "    w, h = img_size\n",
        "    image = tf.image.resize(image, size=(h, w), preserve_aspect_ratio=True)\n",
        "\n",
        "    # Check tha amount of padding needed to be done.\n",
        "    pad_height = h - tf.shape(image)[0]\n",
        "    pad_width = w - tf.shape(image)[1]\n",
        "\n",
        "    # Only necessary if you want to do same amount of padding on both sides.\n",
        "    if pad_height % 2 != 0:\n",
        "        height = pad_height // 2\n",
        "        pad_height_top = height + 1\n",
        "        pad_height_bottom = height\n",
        "    else:\n",
        "        pad_height_top = pad_height_bottom = pad_height // 2\n",
        "\n",
        "    if pad_width % 2 != 0:\n",
        "        width = pad_width // 2\n",
        "        pad_width_left = width + 1\n",
        "        pad_width_right = width\n",
        "    else:\n",
        "        pad_width_left = pad_width_right = pad_width // 2\n",
        "\n",
        "    image = tf.pad(\n",
        "        image,\n",
        "        paddings=[\n",
        "            [pad_height_top, pad_height_bottom],\n",
        "            [pad_width_left, pad_width_right],\n",
        "            [0, 0],\n",
        "        ],\n",
        "    )\n",
        "\n",
        "    image = tf.transpose(image, perm=[1, 0, 2])\n",
        "    image = tf.image.flip_left_right(image)\n",
        "    return image\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ebDrtfy5gSZw"
      },
      "source": [
        "If we just go with the plain resizing then the images would look like so:\n",
        "\n",
        "![](https://i.imgur.com/eqq3s4N.png)\n",
        "\n",
        "Notice how this resizing would have introduced unnecessary stretching."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mfGKWdyCgSZ1"
      },
      "source": [
        "### Putting the utilities together"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "YziRnLPIgSZ2"
      },
      "outputs": [],
      "source": [
        "batch_size = 64\n",
        "padding_token = 99\n",
        "image_width = 128\n",
        "image_height = 32\n",
        "\n",
        "\n",
        "def preprocess_image(image_path, img_size=(image_width, image_height)):\n",
        "    image = tf.io.read_file(image_path)\n",
        "    image = tf.image.decode_png(image, 1)\n",
        "    image = distortion_free_resize(image, img_size)\n",
        "    image = tf.cast(image, tf.float32) / 255.0\n",
        "    return image\n",
        "\n",
        "\n",
        "def vectorize_label(label):\n",
        "    label = char_to_num(tf.strings.unicode_split(label, input_encoding=\"UTF-8\"))\n",
        "    length = tf.shape(label)[0]\n",
        "    pad_amount = max_len - length\n",
        "    label = tf.pad(label, paddings=[[0, pad_amount]], constant_values=padding_token)\n",
        "    return label\n",
        "\n",
        "\n",
        "def process_images_labels(image_path, label):\n",
        "    image = preprocess_image(image_path)\n",
        "    label = vectorize_label(label)\n",
        "    return {\"image\": image, \"label\": label}\n",
        "\n",
        "\n",
        "def prepare_dataset(image_paths, labels):\n",
        "    dataset = tf.data.Dataset.from_tensor_slices((image_paths, labels)).map(\n",
        "        process_images_labels, num_parallel_calls=AUTOTUNE\n",
        "    )\n",
        "    return dataset.batch(batch_size).cache().prefetch(AUTOTUNE)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VfBww4a_gSZ4"
      },
      "source": [
        "## Prepare `tf.data.Dataset` objects"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "ZHxvTuS_gSZ5"
      },
      "outputs": [],
      "source": [
        "train_ds = prepare_dataset(train_img_paths, train_labels_cleaned)\n",
        "validation_ds = prepare_dataset(validation_img_paths, validation_labels_cleaned)\n",
        "test_ds = prepare_dataset(test_img_paths, test_labels_cleaned)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pe0QibUGgSZ6"
      },
      "source": [
        "## Visualize a few samples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "H6GW_n7ygSZ7",
        "outputId": "fa035150-1668-4c23-9470-a5700dee40f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 16 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "for data in train_ds.take(1):\n",
        "    images, labels = data[\"image\"], data[\"label\"]\n",
        "\n",
        "    _, ax = plt.subplots(4, 4, figsize=(15, 8))\n",
        "\n",
        "    for i in range(16):\n",
        "        img = images[i]\n",
        "        img = tf.image.flip_left_right(img)\n",
        "        img = tf.transpose(img, perm=[1, 0, 2])\n",
        "        img = (img * 255.0).numpy().clip(0, 255).astype(np.uint8)\n",
        "        img = img[:, :, 0]\n",
        "\n",
        "        # Gather indices where label!= padding_token.\n",
        "        label = labels[i]\n",
        "        indices = tf.gather(label, tf.where(tf.math.not_equal(label, padding_token)))\n",
        "        # Convert to string.\n",
        "        label = tf.strings.reduce_join(num_to_char(indices))\n",
        "        label = label.numpy().decode(\"utf-8\")\n",
        "\n",
        "        ax[i // 4, i % 4].imshow(img, cmap=\"gray\")\n",
        "        ax[i // 4, i % 4].set_title(label)\n",
        "        ax[i // 4, i % 4].axis(\"off\")\n",
        "\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMNO9_d1gSZ8"
      },
      "source": [
        "You will notice that the content of original image is kept as faithful as possible and has\n",
        "been padded accordingly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TPfxRz7YgSZ-"
      },
      "source": [
        "## Model\n",
        "\n",
        "Our model will use the CTC loss as an endpoint layer. For a detailed understanding of the\n",
        "CTC loss, refer to [this post](https://distill.pub/2017/ctc/)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "A9dC1129gSaA",
        "outputId": "43b9ff51-a0fd-4f2e-8cd4-16e84862761a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"handwriting_recognizer\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " image (InputLayer)             [(None, 128, 32, 1)  0           []                               \n",
            "                                ]                                                                 \n",
            "                                                                                                  \n",
            " Conv1 (Conv2D)                 (None, 128, 32, 32)  320         ['image[0][0]']                  \n",
            "                                                                                                  \n",
            " pool1 (MaxPooling2D)           (None, 64, 16, 32)   0           ['Conv1[0][0]']                  \n",
            "                                                                                                  \n",
            " Conv2 (Conv2D)                 (None, 64, 16, 64)   18496       ['pool1[0][0]']                  \n",
            "                                                                                                  \n",
            " pool2 (MaxPooling2D)           (None, 32, 8, 64)    0           ['Conv2[0][0]']                  \n",
            "                                                                                                  \n",
            " reshape (Reshape)              (None, 32, 512)      0           ['pool2[0][0]']                  \n",
            "                                                                                                  \n",
            " dense1 (Dense)                 (None, 32, 64)       32832       ['reshape[0][0]']                \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 32, 64)       0           ['dense1[0][0]']                 \n",
            "                                                                                                  \n",
            " bidirectional (Bidirectional)  (None, 32, 256)      197632      ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            " bidirectional_1 (Bidirectional  (None, 32, 128)     164352      ['bidirectional[0][0]']          \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " label (InputLayer)             [(None, None)]       0           []                               \n",
            "                                                                                                  \n",
            " dense2 (Dense)                 (None, 32, 81)       10449       ['bidirectional_1[0][0]']        \n",
            "                                                                                                  \n",
            " ctc_loss (CTCLayer)            (None, 32, 81)       0           ['label[0][0]',                  \n",
            "                                                                  'dense2[0][0]']                 \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 424,081\n",
            "Trainable params: 424,081\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "\n",
        "class CTCLayer(keras.layers.Layer):\n",
        "    def __init__(self, name=None):\n",
        "        super().__init__(name=name)\n",
        "        self.loss_fn = keras.backend.ctc_batch_cost\n",
        "\n",
        "    def call(self, y_true, y_pred):\n",
        "        batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n",
        "        input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n",
        "        label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n",
        "\n",
        "        input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
        "        label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n",
        "        loss = self.loss_fn(y_true, y_pred, input_length, label_length)\n",
        "        self.add_loss(loss)\n",
        "\n",
        "        # At test time, just return the computed predictions.\n",
        "        return y_pred\n",
        "\n",
        "\n",
        "def build_model():\n",
        "    # Inputs to the model\n",
        "    input_img = keras.Input(shape=(image_width, image_height, 1), name=\"image\")\n",
        "    labels = keras.layers.Input(name=\"label\", shape=(None,))\n",
        "\n",
        "    # First conv block.\n",
        "    x = keras.layers.Conv2D(\n",
        "        32,\n",
        "        (3, 3),\n",
        "        activation=\"relu\",\n",
        "        kernel_initializer=\"he_normal\",\n",
        "        padding=\"same\",\n",
        "        name=\"Conv1\",\n",
        "    )(input_img)\n",
        "    x = keras.layers.MaxPooling2D((2, 2), name=\"pool1\")(x)\n",
        "\n",
        "    # Second conv block.\n",
        "    x = keras.layers.Conv2D(\n",
        "        64,\n",
        "        (3, 3),\n",
        "        activation=\"relu\",\n",
        "        kernel_initializer=\"he_normal\",\n",
        "        padding=\"same\",\n",
        "        name=\"Conv2\",\n",
        "    )(x)\n",
        "    x = keras.layers.MaxPooling2D((2, 2), name=\"pool2\")(x)\n",
        "\n",
        "    # We have used two max pool with pool size and strides 2.\n",
        "    # Hence, downsampled feature maps are 4x smaller. The number of\n",
        "    # filters in the last layer is 64. Reshape accordingly before\n",
        "    # passing the output to the RNN part of the model.\n",
        "    new_shape = ((image_width // 4), (image_height // 4) * 64)\n",
        "    x = keras.layers.Reshape(target_shape=new_shape, name=\"reshape\")(x)\n",
        "    x = keras.layers.Dense(64, activation=\"relu\", name=\"dense1\")(x)\n",
        "    x = keras.layers.Dropout(0.2)(x)\n",
        "\n",
        "    # RNNs.\n",
        "    x = keras.layers.Bidirectional(\n",
        "        keras.layers.LSTM(128, return_sequences=True, dropout=0.25)\n",
        "    )(x)\n",
        "    x = keras.layers.Bidirectional(\n",
        "        keras.layers.LSTM(64, return_sequences=True, dropout=0.25)\n",
        "    )(x)\n",
        "\n",
        "    # +2 is to account for the two special tokens introduced by the CTC loss.\n",
        "    # The recommendation comes here: https://git.io/J0eXP.\n",
        "    x = keras.layers.Dense(\n",
        "        len(char_to_num.get_vocabulary()) + 2, activation=\"softmax\", name=\"dense2\"\n",
        "    )(x)\n",
        "\n",
        "    # Add CTC layer for calculating CTC loss at each step.\n",
        "    output = CTCLayer(name=\"ctc_loss\")(labels, x)\n",
        "\n",
        "    # Define the model.\n",
        "    model = keras.models.Model(\n",
        "        inputs=[input_img, labels], outputs=output, name=\"handwriting_recognizer\"\n",
        "    )\n",
        "    # Optimizer.\n",
        "    opt = keras.optimizers.Adam()\n",
        "    # Compile the model and return.\n",
        "    model.compile(optimizer=opt)\n",
        "    return model\n",
        "\n",
        "\n",
        "# Get the model.\n",
        "model = build_model()\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EsBt1EDIgSaG"
      },
      "source": [
        "## Evaluation metric\n",
        "\n",
        "[Edit Distance](https://en.wikipedia.org/wiki/Edit_distance)\n",
        "is the most widely used metric for evaluating OCR models. In this section, we will\n",
        "implement it and use it as a callback to monitor our model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4HaYGmAmgSaH"
      },
      "source": [
        "We first segregate the validation images and their labels for convenience."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "a2BtW0rkgSaI"
      },
      "outputs": [],
      "source": [
        "validation_images = []\n",
        "validation_labels = []\n",
        "\n",
        "for batch in validation_ds:\n",
        "    validation_images.append(batch[\"image\"])\n",
        "    validation_labels.append(batch[\"label\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2MjRdKfigSaI"
      },
      "source": [
        "Now, we create a callback to monitor the edit distances."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "Z5xee7mVgSaJ"
      },
      "outputs": [],
      "source": [
        "\n",
        "def calculate_edit_distance(labels, predictions):\n",
        "    # Get a single batch and convert its labels to sparse tensors.\n",
        "    saprse_labels = tf.cast(tf.sparse.from_dense(labels), dtype=tf.int64)\n",
        "\n",
        "    # Make predictions and convert them to sparse tensors.\n",
        "    input_len = np.ones(predictions.shape[0]) * predictions.shape[1]\n",
        "    predictions_decoded = keras.backend.ctc_decode(\n",
        "        predictions, input_length=input_len, greedy=True\n",
        "    )[0][0][:, :max_len]\n",
        "    sparse_predictions = tf.cast(\n",
        "        tf.sparse.from_dense(predictions_decoded), dtype=tf.int64\n",
        "    )\n",
        "\n",
        "    # Compute individual edit distances and average them out.\n",
        "    edit_distances = tf.edit_distance(\n",
        "        sparse_predictions, saprse_labels, normalize=False\n",
        "    )\n",
        "    return tf.reduce_mean(edit_distances)\n",
        "\n",
        "\n",
        "class EditDistanceCallback(keras.callbacks.Callback):\n",
        "    def __init__(self, pred_model):\n",
        "        super().__init__()\n",
        "        self.prediction_model = pred_model\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        edit_distances = []\n",
        "\n",
        "        for i in range(len(validation_images)):\n",
        "            labels = validation_labels[i]\n",
        "            predictions = self.prediction_model.predict(validation_images[i])\n",
        "            edit_distances.append(calculate_edit_distance(labels, predictions).numpy())\n",
        "\n",
        "        print(\n",
        "            f\"Mean edit distance for epoch {epoch + 1}: {np.mean(edit_distances):.4f}\"\n",
        "        )\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2myVV0LFgSaK"
      },
      "source": [
        "## Training\n",
        "\n",
        "Now we are ready to kick off model training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "wThJkrK_gSaL",
        "outputId": "8365c5e9-a261-4f33-b688-9d0bafa8c1e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 2s 93ms/step\n",
            "2/2 [==============================] - 0s 91ms/step\n",
            "2/2 [==============================] - 0s 91ms/step\n",
            "2/2 [==============================] - 0s 90ms/step\n",
            "2/2 [==============================] - 0s 90ms/step\n",
            "2/2 [==============================] - 0s 103ms/step\n",
            "2/2 [==============================] - 0s 88ms/step\n",
            "2/2 [==============================] - 0s 95ms/step\n",
            "2/2 [==============================] - 0s 112ms/step\n",
            "2/2 [==============================] - 0s 92ms/step\n",
            "2/2 [==============================] - 0s 92ms/step\n",
            "2/2 [==============================] - 0s 91ms/step\n",
            "2/2 [==============================] - 0s 110ms/step\n",
            "2/2 [==============================] - 0s 92ms/step\n",
            "2/2 [==============================] - 0s 96ms/step\n",
            "2/2 [==============================] - 0s 92ms/step\n",
            "2/2 [==============================] - 0s 92ms/step\n",
            "2/2 [==============================] - 0s 98ms/step\n",
            "2/2 [==============================] - 0s 90ms/step\n",
            "2/2 [==============================] - 0s 89ms/step\n",
            "2/2 [==============================] - 0s 91ms/step\n",
            "2/2 [==============================] - 0s 91ms/step\n",
            "2/2 [==============================] - 0s 103ms/step\n",
            "2/2 [==============================] - 0s 94ms/step\n",
            "2/2 [==============================] - 0s 90ms/step\n",
            "2/2 [==============================] - 0s 91ms/step\n",
            "2/2 [==============================] - 0s 102ms/step\n",
            "2/2 [==============================] - 0s 99ms/step\n",
            "2/2 [==============================] - 0s 90ms/step\n",
            "2/2 [==============================] - 0s 108ms/step\n",
            "2/2 [==============================] - 0s 92ms/step\n",
            "2/2 [==============================] - 0s 89ms/step\n",
            "2/2 [==============================] - 0s 94ms/step\n",
            "2/2 [==============================] - 0s 93ms/step\n",
            "2/2 [==============================] - 0s 98ms/step\n",
            "2/2 [==============================] - 0s 91ms/step\n",
            "2/2 [==============================] - 0s 110ms/step\n",
            "2/2 [==============================] - 0s 96ms/step\n",
            "2/2 [==============================] - 0s 93ms/step\n",
            "2/2 [==============================] - 0s 106ms/step\n",
            "2/2 [==============================] - 0s 91ms/step\n",
            "2/2 [==============================] - 0s 95ms/step\n",
            "2/2 [==============================] - 0s 88ms/step\n",
            "2/2 [==============================] - 0s 100ms/step\n",
            "2/2 [==============================] - 0s 95ms/step\n",
            "2/2 [==============================] - 0s 94ms/step\n",
            "2/2 [==============================] - 0s 102ms/step\n",
            "2/2 [==============================] - 0s 94ms/step\n",
            "2/2 [==============================] - 0s 91ms/step\n",
            "2/2 [==============================] - 0s 90ms/step\n",
            "2/2 [==============================] - 0s 104ms/step\n",
            "2/2 [==============================] - 0s 98ms/step\n",
            "2/2 [==============================] - 0s 92ms/step\n",
            "2/2 [==============================] - 0s 95ms/step\n",
            "2/2 [==============================] - 0s 90ms/step\n",
            "2/2 [==============================] - 0s 102ms/step\n",
            "2/2 [==============================] - 0s 95ms/step\n",
            "2/2 [==============================] - 0s 98ms/step\n",
            "2/2 [==============================] - 0s 92ms/step\n",
            "2/2 [==============================] - 0s 90ms/step\n",
            "2/2 [==============================] - 0s 92ms/step\n",
            "2/2 [==============================] - 0s 98ms/step\n",
            "2/2 [==============================] - 0s 96ms/step\n",
            "2/2 [==============================] - 0s 102ms/step\n",
            "2/2 [==============================] - 0s 97ms/step\n",
            "2/2 [==============================] - 0s 93ms/step\n",
            "2/2 [==============================] - 0s 91ms/step\n",
            "2/2 [==============================] - 0s 89ms/step\n",
            "2/2 [==============================] - 0s 91ms/step\n",
            "2/2 [==============================] - 0s 96ms/step\n",
            "2/2 [==============================] - 0s 90ms/step\n",
            "2/2 [==============================] - 0s 101ms/step\n",
            "2/2 [==============================] - 0s 95ms/step\n",
            "2/2 [==============================] - 0s 95ms/step\n",
            "2/2 [==============================] - 0s 90ms/step\n",
            "1/1 [==============================] - 2s 2s/step\n",
            "Mean edit distance for epoch 1: 20.5099\n",
            "1357/1357 [==============================] - 931s 680ms/step - loss: 13.7125 - val_loss: 11.8045\n"
          ]
        }
      ],
      "source": [
        "epochs = 1  # To get good results this should be at least 50.\n",
        "\n",
        "model = build_model()\n",
        "prediction_model = keras.models.Model(\n",
        "    model.get_layer(name=\"image\").input, model.get_layer(name=\"dense2\").output\n",
        ")\n",
        "edit_distance_callback = EditDistanceCallback(prediction_model)\n",
        "\n",
        "# Train the model.\n",
        "history = model.fit(\n",
        "    train_ds,\n",
        "    validation_data=validation_ds,\n",
        "    epochs=epochs,\n",
        "    callbacks=[edit_distance_callback],\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ex8nm7qbgSaM"
      },
      "source": [
        "## Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "hjeDVT9RgSaN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "51073022-9999-4d7f-dc96-84c80d2c84dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 99ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 16 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "\n",
        "# A utility function to decode the output of the network.\n",
        "def decode_batch_predictions(pred):\n",
        "    input_len = np.ones(pred.shape[0]) * pred.shape[1]\n",
        "    # Use greedy search. For complex tasks, you can use beam search.\n",
        "    results = keras.backend.ctc_decode(pred, input_length=input_len, greedy=True)[0][0][\n",
        "        :, :max_len\n",
        "    ]\n",
        "    # Iterate over the results and get back the text.\n",
        "    output_text = []\n",
        "    for res in results:\n",
        "        res = tf.gather(res, tf.where(tf.math.not_equal(res, -1)))\n",
        "        res = tf.strings.reduce_join(num_to_char(res)).numpy().decode(\"utf-8\")\n",
        "        output_text.append(res)\n",
        "    return output_text\n",
        "\n",
        "\n",
        "#  Let's check results on some test samples.\n",
        "for batch in test_ds.take(1):\n",
        "    batch_images = batch[\"image\"]\n",
        "    _, ax = plt.subplots(4, 4, figsize=(15, 8))\n",
        "\n",
        "    preds = prediction_model.predict(batch_images)\n",
        "    pred_texts = decode_batch_predictions(preds)\n",
        "\n",
        "    for i in range(16):\n",
        "        img = batch_images[i]\n",
        "        img = tf.image.flip_left_right(img)\n",
        "        img = tf.transpose(img, perm=[1, 0, 2])\n",
        "        img = (img * 255.0).numpy().clip(0, 255).astype(np.uint8)\n",
        "        img = img[:, :, 0]\n",
        "\n",
        "        title = f\"Prediction: {pred_texts[i]}\"\n",
        "        ax[i // 4, i % 4].imshow(img, cmap=\"gray\")\n",
        "        ax[i // 4, i % 4].set_title(title)\n",
        "        ax[i // 4, i % 4].axis(\"off\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JEJWae_HgSaO"
      },
      "source": [
        "To get better results the model should be trained for at least 50 epochs."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "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": "3.7.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}