image-ocr.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "image_ocr.ipynb",
      "version": "0.3.2",
      "views": {},
      "default_view": {},
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "jgoI5JMO1zcX",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 2570
        },
        "outputId": "83e9812b-50d1-47ae-ba1b-5e8a0eae29bc",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1526105928576,
          "user_tz": -480,
          "elapsed": 17502,
          "user": {
            "displayName": "Chengwei Zhang",
            "photoUrl": "//lh5.googleusercontent.com/-FK2ckwmh6mM/AAAAAAAAAAI/AAAAAAAAPLw/SX9b1QAzJ5g/s50-c-k-no/photo.jpg",
            "userId": "114808171854651597062"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install -q cairocffi editdistance\n",
        "!apt install -q libcairo2-dev"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists...\n",
            "Building dependency tree...\n",
            "Reading state information...\n",
            "The following additional packages will be installed:\n",
            "  libcairo-gobject2 libcairo-script-interpreter2 libcairo2 libelf1\n",
            "  libglib2.0-bin libglib2.0-data libglib2.0-dev libglib2.0-dev-bin libice-dev\n",
            "  liblzo2-2 libpcre16-3 libpcre3-dev libpcre32-3 libpcrecpp0v5 libpixman-1-0\n",
            "  libpixman-1-dev libsm-dev libxcb-render0 libxcb-render0-dev libxcb-shm0\n",
            "  libxcb-shm0-dev libxext-dev x11proto-xext-dev\n",
            "Suggested packages:\n",
            "  libcairo2-doc libglib2.0-doc libice-doc libsm-doc libxext-doc\n",
            "The following NEW packages will be installed:\n",
            "  libcairo-gobject2 libcairo-script-interpreter2 libcairo2 libcairo2-dev\n",
            "  libelf1 libglib2.0-bin libglib2.0-data libglib2.0-dev libglib2.0-dev-bin\n",
            "  libice-dev liblzo2-2 libpcre16-3 libpcre3-dev libpcre32-3 libpcrecpp0v5\n",
            "  libpixman-1-0 libpixman-1-dev libsm-dev libxcb-render0 libxcb-render0-dev\n",
            "  libxcb-shm0 libxcb-shm0-dev libxext-dev x11proto-xext-dev\n",
            "0 upgraded, 24 newly installed, 0 to remove and 0 not upgraded.\n",
            "Need to get 4,569 kB of archives.\n",
            "After this operation, 22.0 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu artful/main amd64 liblzo2-2 amd64 2.08-1.2 [48.7 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu artful/main amd64 libelf1 amd64 0.170-0.1 [44.3 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu artful/main amd64 libglib2.0-data all 2.54.1-1ubuntu1 [4,250 B]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu artful/main amd64 libpixman-1-0 amd64 0.34.0-1 [230 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu artful/main amd64 libxcb-render0 amd64 1.12-1ubuntu1 [14.8 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu artful/main amd64 libxcb-shm0 amd64 1.12-1ubuntu1 [5,482 B]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu artful/main amd64 libcairo2 amd64 1.14.10-1ubuntu1 [558 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu artful/main amd64 libcairo-gobject2 amd64 1.14.10-1ubuntu1 [17.1 kB]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu artful/main amd64 libcairo-script-interpreter2 amd64 1.14.10-1ubuntu1 [54.4 kB]\n",
            "Get:10 http://archive.ubuntu.com/ubuntu artful/main amd64 x11proto-xext-dev all 7.3.0-1 [212 kB]\n",
            "Get:11 http://archive.ubuntu.com/ubuntu artful/main amd64 libxext-dev amd64 2:1.3.3-1 [82.1 kB]\n",
            "Get:12 http://archive.ubuntu.com/ubuntu artful/main amd64 libice-dev amd64 2:1.0.9-2 [46.8 kB]\n",
            "Get:13 http://archive.ubuntu.com/ubuntu artful/main amd64 libsm-dev amd64 2:1.2.2-1 [16.2 kB]\n",
            "Get:14 http://archive.ubuntu.com/ubuntu artful/main amd64 libpixman-1-dev amd64 0.34.0-1 [245 kB]\n",
            "Get:15 http://archive.ubuntu.com/ubuntu artful/main amd64 libxcb-render0-dev amd64 1.12-1ubuntu1 [18.3 kB]\n",
            "Get:16 http://archive.ubuntu.com/ubuntu artful/main amd64 libxcb-shm0-dev amd64 1.12-1ubuntu1 [6,578 B]\n",
            "Get:17 http://archive.ubuntu.com/ubuntu artful/main amd64 libglib2.0-bin amd64 2.54.1-1ubuntu1 [69.8 kB]\n",
            "Get:18 http://archive.ubuntu.com/ubuntu artful/main amd64 libglib2.0-dev-bin amd64 2.54.1-1ubuntu1 [84.7 kB]\n",
            "Get:19 http://archive.ubuntu.com/ubuntu artful/main amd64 libpcre16-3 amd64 2:8.39-5ubuntu3 [147 kB]\n",
            "Get:20 http://archive.ubuntu.com/ubuntu artful/main amd64 libpcre32-3 amd64 2:8.39-5ubuntu3 [138 kB]\n",
            "Get:21 http://archive.ubuntu.com/ubuntu artful/main amd64 libpcrecpp0v5 amd64 2:8.39-5ubuntu3 [15.2 kB]\n",
            "Get:22 http://archive.ubuntu.com/ubuntu artful/main amd64 libpcre3-dev amd64 2:8.39-5ubuntu3 [536 kB]\n",
            "Get:23 http://archive.ubuntu.com/ubuntu artful/main amd64 libglib2.0-dev amd64 2.54.1-1ubuntu1 [1,373 kB]\n",
            "Get:24 http://archive.ubuntu.com/ubuntu artful/main amd64 libcairo2-dev amd64 1.14.10-1ubuntu1 [601 kB]\n",
            "Fetched 4,569 kB in 1s (3,933 kB/s)\n",
            "\n",
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          "text": [
            "Unpacking libcairo2:amd64 (1.14.10-1ubuntu1) ...\n",
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            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 19%]\u001b[49m\u001b[39m [###########...............................................] \u001b8Selecting previously unselected package libcairo-script-interpreter2:amd64.\n",
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            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 56%]\u001b[49m\u001b[39m [################################..........................] \u001b8Selecting previously unselected package libcairo2-dev.\n",
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            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 57%]\u001b[49m\u001b[39m [#################################.........................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 58%]\u001b[49m\u001b[39m [#################################.........................] \u001b8Setting up libxcb-render0:amd64 (1.12-1ubuntu1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 59%]\u001b[49m\u001b[39m [##################################........................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 60%]\u001b[49m\u001b[39m [##################################........................] \u001b8Setting up libglib2.0-dev-bin (2.54.1-1ubuntu1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 61%]\u001b[49m\u001b[39m [###################################.......................] \u001b8Setting up libelf1:amd64 (0.170-0.1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 62%]\u001b[49m\u001b[39m [####################################......................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 63%]\u001b[49m\u001b[39m [####################################......................] \u001b8Processing triggers for libglib2.0-0:amd64 (2.54.1-1ubuntu1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 64%]\u001b[49m\u001b[39m [#####################################.....................] \u001b8No schema files found: doing nothing.\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 65%]\u001b[49m\u001b[39m [#####################################.....................] \u001b8Setting up libxcb-render0-dev:amd64 (1.12-1ubuntu1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 66%]\u001b[49m\u001b[39m [######################################....................] \u001b8Setting up libpixman-1-0:amd64 (0.34.0-1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 67%]\u001b[49m\u001b[39m [#######################################...................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 68%]\u001b[49m\u001b[39m [#######################################...................] \u001b8Setting up x11proto-xext-dev (7.3.0-1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 69%]\u001b[49m\u001b[39m [########################################..................] \u001b8Setting up libglib2.0-data (2.54.1-1ubuntu1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 70%]\u001b[49m\u001b[39m [#########################################.................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 71%]\u001b[49m\u001b[39m [#########################################.................] \u001b8Processing triggers for libc-bin (2.26-0ubuntu2.1) ...\n",
            "Setting up libice-dev:amd64 (2:1.0.9-2) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 72%]\u001b[49m\u001b[39m [#########################################.................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 73%]\u001b[49m\u001b[39m [##########################################................] \u001b8Setting up libpixman-1-dev (0.34.0-1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 74%]\u001b[49m\u001b[39m [###########################################...............] \u001b8Setting up libxext-dev:amd64 (2:1.3.3-1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 75%]\u001b[49m\u001b[39m [###########################################...............] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 76%]\u001b[49m\u001b[39m [############################################..............] \u001b8Setting up libxcb-shm0:amd64 (1.12-1ubuntu1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 77%]\u001b[49m\u001b[39m [############################################..............] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 78%]\u001b[49m\u001b[39m [#############################################.............] \u001b8Setting up libpcrecpp0v5:amd64 (2:8.39-5ubuntu3) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 79%]\u001b[49m\u001b[39m [##############################################............] \u001b8Setting up libpcre32-3:amd64 (2:8.39-5ubuntu3) ...\n",
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            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 82%]\u001b[49m\u001b[39m [###############################################...........] \u001b8Setting up libglib2.0-bin (2.54.1-1ubuntu1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 83%]\u001b[49m\u001b[39m [################################################..........] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 84%]\u001b[49m\u001b[39m [#################################################.........] \u001b8Setting up liblzo2-2:amd64 (2.08-1.2) ...\n",
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            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 87%]\u001b[49m\u001b[39m [##################################################........] \u001b8Setting up libsm-dev:amd64 (2:1.2.2-1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 88%]\u001b[49m\u001b[39m [###################################################.......] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 89%]\u001b[49m\u001b[39m [###################################################.......] \u001b8Setting up libpcre3-dev:amd64 (2:8.39-5ubuntu3) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 90%]\u001b[49m\u001b[39m [####################################################......] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 91%]\u001b[49m\u001b[39m [####################################################......] \u001b8Setting up libcairo-script-interpreter2:amd64 (1.14.10-1ubuntu1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 92%]\u001b[49m\u001b[39m [#####################################################.....] \u001b8Setting up libcairo-gobject2:amd64 (1.14.10-1ubuntu1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 93%]\u001b[49m\u001b[39m [######################################################....] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 94%]\u001b[49m\u001b[39m [######################################################....] \u001b8Setting up libxcb-shm0-dev:amd64 (1.12-1ubuntu1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 95%]\u001b[49m\u001b[39m [#######################################################...] \u001b8Setting up libglib2.0-dev:amd64 (2.54.1-1ubuntu1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 96%]\u001b[49m\u001b[39m [########################################################..] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 97%]\u001b[49m\u001b[39m [########################################################..] \u001b8Setting up libcairo2-dev (1.14.10-1ubuntu1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 98%]\u001b[49m\u001b[39m [#########################################################.] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 99%]\u001b[49m\u001b[39m [#########################################################.] \u001b8Processing triggers for libc-bin (2.26-0ubuntu2.1) ...\n",
            "\n",
            "\u001b7\u001b[0;24r\u001b8\u001b[1A\u001b[J"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NH-B4hb1E00o",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Install dependencies for keras `plot_model()` function\n",
        "restart the runtime after running this cell by\n",
        "\n",
        "`Runtime --> Restart runtime...`\n",
        "\n",
        "Then continue running this notebook."
      ]
    },
    {
      "metadata": {
        "id": "RDh4Dr7K4MEx",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 2519
        },
        "outputId": "1451aa14-f01c-4462-dcfc-bf28479b82ee",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1526105944838,
          "user_tz": -480,
          "elapsed": 16226,
          "user": {
            "displayName": "Chengwei Zhang",
            "photoUrl": "//lh5.googleusercontent.com/-FK2ckwmh6mM/AAAAAAAAAAI/AAAAAAAAPLw/SX9b1QAzJ5g/s50-c-k-no/photo.jpg",
            "userId": "114808171854651597062"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "!apt install -q graphviz\n",
        "!pip install -q pydot\n",
        "!pip install -q matplotlib graphviz pydot"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading package lists...\n",
            "Building dependency tree...\n",
            "Reading state information...\n",
            "The following additional packages will be installed:\n",
            "  fontconfig libcdt5 libcgraph6 libdatrie1 libgd3 libgraphite2-3 libgvc6\n",
            "  libgvpr2 libharfbuzz0b libjbig0 libltdl7 libpango-1.0-0 libpangocairo-1.0-0\n",
            "  libpangoft2-1.0-0 libpathplan4 libthai-data libthai0 libtiff5 libwebp6\n",
            "  libxaw7 libxmu6 libxpm4 libxt6\n",
            "Suggested packages:\n",
            "  gsfonts graphviz-doc libgd-tools\n",
            "The following NEW packages will be installed:\n",
            "  fontconfig graphviz libcdt5 libcgraph6 libdatrie1 libgd3 libgraphite2-3\n",
            "  libgvc6 libgvpr2 libharfbuzz0b libjbig0 libltdl7 libpango-1.0-0\n",
            "  libpangocairo-1.0-0 libpangoft2-1.0-0 libpathplan4 libthai-data libthai0\n",
            "  libtiff5 libwebp6 libxaw7 libxmu6 libxpm4 libxt6\n",
            "0 upgraded, 24 newly installed, 0 to remove and 0 not upgraded.\n",
            "Need to get 3,314 kB of archives.\n",
            "After this operation, 18.5 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu artful/main amd64 fontconfig amd64 2.11.94-0ubuntu2 [177 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu artful/main amd64 libjbig0 amd64 2.1-3.1 [26.6 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu artful/main amd64 libcdt5 amd64 2.38.0-16ubuntu2 [19.5 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu artful/main amd64 libcgraph6 amd64 2.38.0-16ubuntu2 [40.0 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu artful-updates/main amd64 libtiff5 amd64 4.0.8-5ubuntu0.1 [150 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu artful/main amd64 libwebp6 amd64 0.6.0-3 [181 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu artful/main amd64 libxpm4 amd64 1:3.5.12-1 [34.0 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu artful/main amd64 libgd3 amd64 2.2.5-3 [119 kB]\n",
            "Get:9 http://archive.ubuntu.com/ubuntu artful/main amd64 libltdl7 amd64 2.4.6-2 [38.8 kB]\n",
            "Get:10 http://archive.ubuntu.com/ubuntu artful/main amd64 libthai-data all 0.1.26-3 [132 kB]\n",
            "Get:11 http://archive.ubuntu.com/ubuntu artful/main amd64 libdatrie1 amd64 0.2.10-5 [17.6 kB]\n",
            "Get:12 http://archive.ubuntu.com/ubuntu artful/main amd64 libthai0 amd64 0.1.26-3 [17.7 kB]\n",
            "Get:13 http://archive.ubuntu.com/ubuntu artful/main amd64 libpango-1.0-0 amd64 1.40.12-1 [152 kB]\n",
            "Get:14 http://archive.ubuntu.com/ubuntu artful/main amd64 libgraphite2-3 amd64 1.3.10-2 [78.3 kB]\n",
            "Get:15 http://archive.ubuntu.com/ubuntu artful/main amd64 libharfbuzz0b amd64 1.4.2-1 [211 kB]\n",
            "Get:16 http://archive.ubuntu.com/ubuntu artful/main amd64 libpangoft2-1.0-0 amd64 1.40.12-1 [33.2 kB]\n",
            "Get:17 http://archive.ubuntu.com/ubuntu artful/main amd64 libpangocairo-1.0-0 amd64 1.40.12-1 [20.8 kB]\n",
            "Get:18 http://archive.ubuntu.com/ubuntu artful/main amd64 libpathplan4 amd64 2.38.0-16ubuntu2 [22.6 kB]\n",
            "Get:19 http://archive.ubuntu.com/ubuntu artful/main amd64 libgvc6 amd64 2.38.0-16ubuntu2 [587 kB]\n",
            "Get:20 http://archive.ubuntu.com/ubuntu artful/main amd64 libgvpr2 amd64 2.38.0-16ubuntu2 [167 kB]\n",
            "Get:21 http://archive.ubuntu.com/ubuntu artful/main amd64 libxt6 amd64 1:1.1.5-1 [160 kB]\n",
            "Get:22 http://archive.ubuntu.com/ubuntu artful/main amd64 libxmu6 amd64 2:1.1.2-2 [46.0 kB]\n",
            "Get:23 http://archive.ubuntu.com/ubuntu artful/main amd64 libxaw7 amd64 2:1.0.13-1 [173 kB]\n",
            "Get:24 http://archive.ubuntu.com/ubuntu artful/main amd64 graphviz amd64 2.38.0-16ubuntu2 [710 kB]\n",
            "Fetched 3,314 kB in 1s (3,039 kB/s)\n",
            "\n",
            "\u001b7\u001b[0;23r\u001b8\u001b[1ASelecting previously unselected package fontconfig.\n",
            "(Reading database ... 19094 files and directories currently installed.)\n",
            "Preparing to unpack .../00-fontconfig_2.11.94-0ubuntu2_amd64.deb ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  0%]\u001b[49m\u001b[39m [..........................................................] \u001b8Unpacking fontconfig (2.11.94-0ubuntu2) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  1%]\u001b[49m\u001b[39m [..........................................................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  2%]\u001b[49m\u001b[39m [#.........................................................] \u001b8Selecting previously unselected package libjbig0:amd64.\n",
            "Preparing to unpack .../01-libjbig0_2.1-3.1_amd64.deb ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  3%]\u001b[49m\u001b[39m [#.........................................................] \u001b8Unpacking libjbig0:amd64 (2.1-3.1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  4%]\u001b[49m\u001b[39m [##........................................................] \u001b8Selecting previously unselected package libcdt5.\n",
            "Preparing to unpack .../02-libcdt5_2.38.0-16ubuntu2_amd64.deb ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  5%]\u001b[49m\u001b[39m [###.......................................................] \u001b8Unpacking libcdt5 (2.38.0-16ubuntu2) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  6%]\u001b[49m\u001b[39m [###.......................................................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  7%]\u001b[49m\u001b[39m [####......................................................] \u001b8Selecting previously unselected package libcgraph6.\n",
            "Preparing to unpack .../03-libcgraph6_2.38.0-16ubuntu2_amd64.deb ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  8%]\u001b[49m\u001b[39m [####......................................................] \u001b8Unpacking libcgraph6 (2.38.0-16ubuntu2) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [  9%]\u001b[49m\u001b[39m [#####.....................................................] \u001b8Selecting previously unselected package libtiff5:amd64.\n",
            "Preparing to unpack .../04-libtiff5_4.0.8-5ubuntu0.1_amd64.deb ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 10%]\u001b[49m\u001b[39m [######....................................................] \u001b8Unpacking libtiff5:amd64 (4.0.8-5ubuntu0.1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 11%]\u001b[49m\u001b[39m [######....................................................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 12%]\u001b[49m\u001b[39m [#######...................................................] \u001b8Selecting previously unselected package libwebp6:amd64.\n",
            "Preparing to unpack .../05-libwebp6_0.6.0-3_amd64.deb ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 13%]\u001b[49m\u001b[39m [#######...................................................] \u001b8Unpacking libwebp6:amd64 (0.6.0-3) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 14%]\u001b[49m\u001b[39m [########..................................................] \u001b8Selecting previously unselected package libxpm4:amd64.\n",
            "Preparing to unpack .../06-libxpm4_1%3a3.5.12-1_amd64.deb ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 15%]\u001b[49m\u001b[39m [#########.................................................] \u001b8Unpacking libxpm4:amd64 (1:3.5.12-1) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 16%]\u001b[49m\u001b[39m [#########.................................................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 17%]\u001b[49m\u001b[39m [##########................................................] \u001b8"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Selecting previously unselected package libgd3:amd64.\r\n",
            "Preparing to unpack .../07-libgd3_2.2.5-3_amd64.deb ...\r\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 18%]\u001b[49m\u001b[39m [##########................................................] \u001b8Unpacking libgd3:amd64 (2.2.5-3) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 19%]\u001b[49m\u001b[39m [###########...............................................] \u001b8Selecting previously unselected package libltdl7:amd64.\n",
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            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 20%]\u001b[49m\u001b[39m [###########...............................................] \u001b8Unpacking libltdl7:amd64 (2.4.6-2) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 21%]\u001b[49m\u001b[39m [############..............................................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 22%]\u001b[49m\u001b[39m [############..............................................] \u001b8Selecting previously unselected package libthai-data.\n",
            "Preparing to unpack .../09-libthai-data_0.1.26-3_all.deb ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 23%]\u001b[49m\u001b[39m [#############.............................................] \u001b8Unpacking libthai-data (0.1.26-3) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 24%]\u001b[49m\u001b[39m [##############............................................] \u001b8Selecting previously unselected package libdatrie1:amd64.\n",
            "Preparing to unpack .../10-libdatrie1_0.2.10-5_amd64.deb ...\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 56%]\u001b[49m\u001b[39m [################################..........................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 57%]\u001b[49m\u001b[39m [#################################.........................] \u001b8Selecting previously unselected package graphviz.\n",
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            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 58%]\u001b[49m\u001b[39m [##################################........................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 59%]\u001b[49m\u001b[39m [##################################........................] \u001b8Setting up libpathplan4 (2.38.0-16ubuntu2) ...\n",
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            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 62%]\u001b[49m\u001b[39m [####################################......................] \u001b8Setting up libdatrie1:amd64 (0.2.10-5) ...\n",
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            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 67%]\u001b[49m\u001b[39m [#######################################...................] \u001b8Processing triggers for libc-bin (2.26-0ubuntu2.1) ...\n",
            "Setting up libltdl7:amd64 (2.4.6-2) ...\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 68%]\u001b[49m\u001b[39m [#######################################...................] \u001b8\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 69%]\u001b[49m\u001b[39m [########################################..................] \u001b8Setting up libxpm4:amd64 (1:3.5.12-1) ...\n",
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            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 72%]\u001b[49m\u001b[39m [##########################################................] \u001b8Setting up libthai-data (0.1.26-3) ...\n",
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            "Regenerating fonts cache... done.\n",
            "\u001b7\u001b[24;0f\u001b[42m\u001b[30mProgress: [ 77%]\u001b[49m\u001b[39m [#############################################.............] \u001b8Setting up libcgraph6 (2.38.0-16ubuntu2) ...\n",
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            "\n",
            "\u001b7\u001b[0;24r\u001b8\u001b[1A\u001b[J"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1wsDPx682A7H",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "88ee055c-b674-4a81-c869-8401551f09d7",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1526105952629,
          "user_tz": -480,
          "elapsed": 7776,
          "user": {
            "displayName": "Chengwei Zhang",
            "photoUrl": "//lh5.googleusercontent.com/-FK2ckwmh6mM/AAAAAAAAAAI/AAAAAAAAPLw/SX9b1QAzJ5g/s50-c-k-no/photo.jpg",
            "userId": "114808171854651597062"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "import itertools\n",
        "import codecs\n",
        "import re\n",
        "import datetime\n",
        "import cairocffi as cairo\n",
        "import editdistance\n",
        "import numpy as np\n",
        "from scipy import ndimage\n",
        "# import pylab\n",
        "import matplotlib.pyplot as plt\n",
        "from keras import backend as K\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.layers import Input, Dense, Activation\n",
        "from keras.layers import Reshape, Lambda\n",
        "from keras.layers.merge import add, concatenate\n",
        "from keras.models import Model\n",
        "from keras.layers.recurrent import GRU\n",
        "from keras.optimizers import SGD\n",
        "from keras.utils.data_utils import get_file\n",
        "from keras.preprocessing import image\n",
        "import keras.callbacks"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "Xnj6R_OE3yMl",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "OUTPUT_DIR = 'image_ocr'\n",
        "\n",
        "# character classes and matching regex filter\n",
        "regex = r'^[a-z ]+$'\n",
        "alphabet = u'abcdefghijklmnopqrstuvwxyz '\n",
        "\n",
        "np.random.seed(55)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mM0eHsJO5bP6",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# this creates larger \"blotches\" of noise which look\n",
        "# more realistic than just adding gaussian noise\n",
        "# assumes greyscale with pixels ranging from 0 to 1\n",
        "\n",
        "def speckle(img):\n",
        "    severity = np.random.uniform(0, 0.6)\n",
        "    blur = ndimage.gaussian_filter(np.random.randn(*img.shape) * severity, 1)\n",
        "    img_speck = (img + blur)\n",
        "    img_speck[img_speck > 1] = 1\n",
        "    img_speck[img_speck <= 0] = 0\n",
        "    return img_speck\n",
        "\n",
        "\n",
        "# paints the string in a random location the bounding box\n",
        "# also uses a random font, a slight random rotation,\n",
        "# and a random amount of speckle noise\n",
        "\n",
        "def paint_text(text, w, h, rotate=False, ud=False, multi_fonts=False):\n",
        "    surface = cairo.ImageSurface(cairo.FORMAT_RGB24, w, h)\n",
        "    with cairo.Context(surface) as context:\n",
        "        context.set_source_rgb(1, 1, 1)  # White\n",
        "        context.paint()\n",
        "        # this font list works in CentOS 7\n",
        "        if multi_fonts:\n",
        "            fonts = ['Century Schoolbook', 'Courier', 'STIX', 'URW Chancery L', 'FreeMono']\n",
        "            context.select_font_face(np.random.choice(fonts), cairo.FONT_SLANT_NORMAL,\n",
        "                                     np.random.choice([cairo.FONT_WEIGHT_BOLD, cairo.FONT_WEIGHT_NORMAL]))\n",
        "        else:\n",
        "            context.select_font_face('Courier', cairo.FONT_SLANT_NORMAL, cairo.FONT_WEIGHT_BOLD)\n",
        "        context.set_font_size(25)\n",
        "        box = context.text_extents(text)\n",
        "        border_w_h = (4, 4)\n",
        "        if box[2] > (w - 2 * border_w_h[1]) or box[3] > (h - 2 * border_w_h[0]):\n",
        "            raise IOError('Could not fit string into image. Max char count is too large for given image width.')\n",
        "\n",
        "        # teach the RNN translational invariance by\n",
        "        # fitting text box randomly on canvas, with some room to rotate\n",
        "        max_shift_x = w - box[2] - border_w_h[0]\n",
        "        max_shift_y = h - box[3] - border_w_h[1]\n",
        "        top_left_x = np.random.randint(0, int(max_shift_x))\n",
        "        if ud:\n",
        "            top_left_y = np.random.randint(0, int(max_shift_y))\n",
        "        else:\n",
        "            top_left_y = h // 2\n",
        "        context.move_to(top_left_x - int(box[0]), top_left_y - int(box[1]))\n",
        "        context.set_source_rgb(0, 0, 0)\n",
        "        context.show_text(text)\n",
        "\n",
        "    buf = surface.get_data()\n",
        "    a = np.frombuffer(buf, np.uint8)\n",
        "    a.shape = (h, w, 4)\n",
        "    a = a[:, :, 0]  # grab single channel\n",
        "    a = a.astype(np.float32) / 255\n",
        "    a = np.expand_dims(a, 0)\n",
        "    if rotate:\n",
        "        a = image.random_rotation(a, 3 * (w - top_left_x) / w + 1)\n",
        "    a = speckle(a)\n",
        "\n",
        "    return a\n",
        "\n",
        "\n",
        "def shuffle_mats_or_lists(matrix_list, stop_ind=None):\n",
        "    ret = []\n",
        "    assert all([len(i) == len(matrix_list[0]) for i in matrix_list])\n",
        "    len_val = len(matrix_list[0])\n",
        "    if stop_ind is None:\n",
        "        stop_ind = len_val\n",
        "    assert stop_ind <= len_val\n",
        "\n",
        "    a = list(range(stop_ind))\n",
        "    np.random.shuffle(a)\n",
        "    a += list(range(stop_ind, len_val))\n",
        "    for mat in matrix_list:\n",
        "        if isinstance(mat, np.ndarray):\n",
        "            ret.append(mat[a])\n",
        "        elif isinstance(mat, list):\n",
        "            ret.append([mat[i] for i in a])\n",
        "        else:\n",
        "            raise TypeError('`shuffle_mats_or_lists` only supports '\n",
        "                            'numpy.array and list objects.')\n",
        "    return ret\n",
        "\n",
        "\n",
        "# Translation of characters to unique integer values\n",
        "def text_to_labels(text):\n",
        "    ret = []\n",
        "    for char in text:\n",
        "        ret.append(alphabet.find(char))\n",
        "    return ret\n",
        "\n",
        "\n",
        "# Reverse translation of numerical classes back to characters\n",
        "def labels_to_text(labels):\n",
        "    ret = []\n",
        "    for c in labels:\n",
        "        if c == len(alphabet):  # CTC Blank\n",
        "            ret.append(\"\")\n",
        "        else:\n",
        "            ret.append(alphabet[c])\n",
        "    return \"\".join(ret)\n",
        "\n",
        "\n",
        "# only a-z and space..probably not to difficult\n",
        "# to expand to uppercase and symbols\n",
        "\n",
        "def is_valid_str(in_str):\n",
        "    search = re.compile(regex, re.UNICODE).search\n",
        "    return bool(search(in_str))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HBLwtxF35f0c",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# Uses generator functions to supply train/test with\n",
        "# data. Image renderings are text are created on the fly\n",
        "# each time with random perturbations\n",
        "\n",
        "class TextImageGenerator(keras.callbacks.Callback):\n",
        "\n",
        "    def __init__(self, monogram_file, bigram_file, minibatch_size,\n",
        "                 img_w, img_h, downsample_factor, val_split,\n",
        "                 absolute_max_string_len=16):\n",
        "\n",
        "        self.minibatch_size = minibatch_size\n",
        "        self.img_w = img_w\n",
        "        self.img_h = img_h\n",
        "        self.monogram_file = monogram_file\n",
        "        self.bigram_file = bigram_file\n",
        "        self.downsample_factor = downsample_factor\n",
        "        self.val_split = val_split\n",
        "        self.blank_label = self.get_output_size() - 1\n",
        "        self.absolute_max_string_len = absolute_max_string_len\n",
        "\n",
        "    def get_output_size(self):\n",
        "        return len(alphabet) + 1\n",
        "\n",
        "    # num_words can be independent of the epoch size due to the use of generators\n",
        "    # as max_string_len grows, num_words can grow\n",
        "    def build_word_list(self, num_words, max_string_len=None, mono_fraction=0.5):\n",
        "        assert max_string_len <= self.absolute_max_string_len\n",
        "        assert num_words % self.minibatch_size == 0\n",
        "        assert (self.val_split * num_words) % self.minibatch_size == 0\n",
        "        self.num_words = num_words\n",
        "        self.string_list = [''] * self.num_words\n",
        "        tmp_string_list = []\n",
        "        self.max_string_len = max_string_len\n",
        "        self.Y_data = np.ones([self.num_words, self.absolute_max_string_len]) * -1\n",
        "        self.X_text = []\n",
        "        self.Y_len = [0] * self.num_words\n",
        "\n",
        "        # monogram file is sorted by frequency in english speech\n",
        "        with codecs.open(self.monogram_file, mode='r', encoding='utf-8') as f:\n",
        "            for line in f:\n",
        "                if len(tmp_string_list) == int(self.num_words * mono_fraction):\n",
        "                    break\n",
        "                word = line.rstrip()\n",
        "                if max_string_len == -1 or max_string_len is None or len(word) <= max_string_len:\n",
        "                    tmp_string_list.append(word)\n",
        "\n",
        "        # bigram file contains common word pairings in english speech\n",
        "        with codecs.open(self.bigram_file, mode='r', encoding='utf-8') as f:\n",
        "            lines = f.readlines()\n",
        "            for line in lines:\n",
        "                if len(tmp_string_list) == self.num_words:\n",
        "                    break\n",
        "                columns = line.lower().split()\n",
        "                word = columns[0] + ' ' + columns[1]\n",
        "                if is_valid_str(word) and \\\n",
        "                        (max_string_len == -1 or max_string_len is None or len(word) <= max_string_len):\n",
        "                    tmp_string_list.append(word)\n",
        "        if len(tmp_string_list) != self.num_words:\n",
        "            raise IOError('Could not pull enough words from supplied monogram and bigram files. ')\n",
        "        # interlace to mix up the easy and hard words\n",
        "        self.string_list[::2] = tmp_string_list[:self.num_words // 2]\n",
        "        self.string_list[1::2] = tmp_string_list[self.num_words // 2:]\n",
        "\n",
        "        for i, word in enumerate(self.string_list):\n",
        "            self.Y_len[i] = len(word)\n",
        "            self.Y_data[i, 0:len(word)] = text_to_labels(word)\n",
        "            self.X_text.append(word)\n",
        "        self.Y_len = np.expand_dims(np.array(self.Y_len), 1)\n",
        "\n",
        "        self.cur_val_index = self.val_split\n",
        "        self.cur_train_index = 0\n",
        "\n",
        "    # each time an image is requested from train/val/test, a new random\n",
        "    # painting of the text is performed\n",
        "    def get_batch(self, index, size, train):\n",
        "        # width and height are backwards from typical Keras convention\n",
        "        # because width is the time dimension when it gets fed into the RNN\n",
        "        if K.image_data_format() == 'channels_first':\n",
        "            X_data = np.ones([size, 1, self.img_w, self.img_h])\n",
        "        else:\n",
        "            X_data = np.ones([size, self.img_w, self.img_h, 1])\n",
        "\n",
        "        labels = np.ones([size, self.absolute_max_string_len])\n",
        "        input_length = np.zeros([size, 1])\n",
        "        label_length = np.zeros([size, 1])\n",
        "        source_str = []\n",
        "        for i in range(size):\n",
        "            # Mix in some blank inputs.  This seems to be important for\n",
        "            # achieving translational invariance\n",
        "            if train and i > size - 4:\n",
        "                if K.image_data_format() == 'channels_first':\n",
        "                    X_data[i, 0, 0:self.img_w, :] = self.paint_func('')[0, :, :].T\n",
        "                else:\n",
        "                    X_data[i, 0:self.img_w, :, 0] = self.paint_func('',)[0, :, :].T\n",
        "                labels[i, 0] = self.blank_label\n",
        "                input_length[i] = self.img_w // self.downsample_factor - 2\n",
        "                label_length[i] = 1\n",
        "                source_str.append('')\n",
        "            else:\n",
        "                if K.image_data_format() == 'channels_first':\n",
        "                    X_data[i, 0, 0:self.img_w, :] = self.paint_func(self.X_text[index + i])[0, :, :].T\n",
        "                else:\n",
        "                    X_data[i, 0:self.img_w, :, 0] = self.paint_func(self.X_text[index + i])[0, :, :].T\n",
        "                labels[i, :] = self.Y_data[index + i]\n",
        "                input_length[i] = self.img_w // self.downsample_factor - 2\n",
        "                label_length[i] = self.Y_len[index + i]\n",
        "                source_str.append(self.X_text[index + i])\n",
        "        inputs = {'the_input': X_data,\n",
        "                  'the_labels': labels,\n",
        "                  'input_length': input_length,\n",
        "                  'label_length': label_length,\n",
        "                  'source_str': source_str  # used for visualization only\n",
        "                  }\n",
        "        outputs = {'ctc': np.zeros([size])}  # dummy data for dummy loss function\n",
        "        return (inputs, outputs)\n",
        "\n",
        "    def next_train(self):\n",
        "        while 1:\n",
        "            ret = self.get_batch(self.cur_train_index, self.minibatch_size, train=True)\n",
        "            self.cur_train_index += self.minibatch_size\n",
        "            if self.cur_train_index >= self.val_split:\n",
        "                self.cur_train_index = self.cur_train_index % 32\n",
        "                (self.X_text, self.Y_data, self.Y_len) = shuffle_mats_or_lists(\n",
        "                    [self.X_text, self.Y_data, self.Y_len], self.val_split)\n",
        "            yield ret\n",
        "\n",
        "    def next_val(self):\n",
        "        while 1:\n",
        "            ret = self.get_batch(self.cur_val_index, self.minibatch_size, train=False)\n",
        "            self.cur_val_index += self.minibatch_size\n",
        "            if self.cur_val_index >= self.num_words:\n",
        "                self.cur_val_index = self.val_split + self.cur_val_index % 32\n",
        "            yield ret\n",
        "\n",
        "    def on_train_begin(self, logs={}):\n",
        "        self.build_word_list(16000, 4, 1)\n",
        "        self.paint_func = lambda text: paint_text(text, self.img_w, self.img_h,\n",
        "                                                  rotate=False, ud=False, multi_fonts=False)\n",
        "\n",
        "    def on_epoch_begin(self, epoch, logs={}):\n",
        "        # rebind the paint function to implement curriculum learning\n",
        "        if 3 <= epoch < 6:\n",
        "            self.paint_func = lambda text: paint_text(text, self.img_w, self.img_h,\n",
        "                                                      rotate=False, ud=True, multi_fonts=False)\n",
        "        elif 6 <= epoch < 9:\n",
        "            self.paint_func = lambda text: paint_text(text, self.img_w, self.img_h,\n",
        "                                                      rotate=False, ud=True, multi_fonts=True)\n",
        "        elif epoch >= 9:\n",
        "            self.paint_func = lambda text: paint_text(text, self.img_w, self.img_h,\n",
        "                                                      rotate=True, ud=True, multi_fonts=True)\n",
        "        if epoch >= 21 and self.max_string_len < 12:\n",
        "            self.build_word_list(32000, 12, 0.5)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YOAsKfXm5jP1",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# the actual loss calc occurs here despite it not being\n",
        "# an internal Keras loss function\n",
        "\n",
        "def ctc_lambda_func(args):\n",
        "    y_pred, labels, input_length, label_length = args\n",
        "    # the 2 is critical here since the first couple outputs of the RNN\n",
        "    # tend to be garbage:\n",
        "    y_pred = y_pred[:, 2:, :]\n",
        "    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)\n",
        "\n",
        "\n",
        "# For a real OCR application, this should be beam search with a dictionary\n",
        "# and language model.  For this example, best path is sufficient.\n",
        "\n",
        "def decode_batch(test_func, word_batch):\n",
        "    out = test_func([word_batch])[0]\n",
        "    ret = []\n",
        "    for j in range(out.shape[0]):\n",
        "        out_best = list(np.argmax(out[j, 2:], 1))\n",
        "        out_best = [k for k, g in itertools.groupby(out_best)]\n",
        "        outstr = labels_to_text(out_best)\n",
        "        ret.append(outstr)\n",
        "    return ret\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KcGtIsLF5leW",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "class VizCallback(keras.callbacks.Callback):\n",
        "\n",
        "    def __init__(self, run_name, test_func, text_img_gen, num_display_words=6):\n",
        "        self.test_func = test_func\n",
        "        self.output_dir = os.path.join(\n",
        "            OUTPUT_DIR, run_name)\n",
        "        self.text_img_gen = text_img_gen\n",
        "        self.num_display_words = num_display_words\n",
        "        if not os.path.exists(self.output_dir):\n",
        "            os.makedirs(self.output_dir)\n",
        "\n",
        "    def show_edit_distance(self, num):\n",
        "        num_left = num\n",
        "        mean_norm_ed = 0.0\n",
        "        mean_ed = 0.0\n",
        "        while num_left > 0:\n",
        "            word_batch = next(self.text_img_gen)[0]\n",
        "            num_proc = min(word_batch['the_input'].shape[0], num_left)\n",
        "            decoded_res = decode_batch(self.test_func, word_batch['the_input'][0:num_proc])\n",
        "            for j in range(num_proc):\n",
        "                edit_dist = editdistance.eval(decoded_res[j], word_batch['source_str'][j])\n",
        "                mean_ed += float(edit_dist)\n",
        "                mean_norm_ed += float(edit_dist) / len(word_batch['source_str'][j])\n",
        "            num_left -= num_proc\n",
        "        mean_norm_ed = mean_norm_ed / num\n",
        "        mean_ed = mean_ed / num\n",
        "        print('\\nOut of %d samples:  Mean edit distance: %.3f Mean normalized edit distance: %0.3f'\n",
        "              % (num, mean_ed, mean_norm_ed))\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs={}):\n",
        "        self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))\n",
        "        self.show_edit_distance(256)\n",
        "        word_batch = next(self.text_img_gen)[0]\n",
        "        res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])\n",
        "        if word_batch['the_input'][0].shape[0] < 256:\n",
        "            cols = 2\n",
        "        else:\n",
        "            cols = 1\n",
        "        for i in range(self.num_display_words):\n",
        "            plt.subplot(self.num_display_words // cols, cols, i + 1)\n",
        "            if K.image_data_format() == 'channels_first':\n",
        "                the_input = word_batch['the_input'][i, 0, :, :]\n",
        "            else:\n",
        "                the_input = word_batch['the_input'][i, :, :, 0]\n",
        "            plt.imshow(the_input.T, cmap='Greys_r')\n",
        "            plt.xlabel('Truth = \\'%s\\'\\nDecoded = \\'%s\\'' % (word_batch['source_str'][i], res[i]))\n",
        "        fig = pylab.gcf()\n",
        "        fig.set_size_inches(10, 13)\n",
        "        plt.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))\n",
        "        plt.close()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eC4vEfcQ5oOq",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "def train(run_name, start_epoch, stop_epoch, img_w):\n",
        "    # Input Parameters\n",
        "    img_h = 64\n",
        "    words_per_epoch = 16000\n",
        "    val_split = 0.2\n",
        "    val_words = int(words_per_epoch * (val_split))\n",
        "\n",
        "    # Network parameters\n",
        "    conv_filters = 16\n",
        "    kernel_size = (3, 3)\n",
        "    pool_size = 2\n",
        "    time_dense_size = 32\n",
        "    rnn_size = 512\n",
        "    minibatch_size = 32\n",
        "\n",
        "    if K.image_data_format() == 'channels_first':\n",
        "        input_shape = (1, img_w, img_h)\n",
        "    else:\n",
        "        input_shape = (img_w, img_h, 1)\n",
        "\n",
        "    fdir = os.path.dirname(get_file('wordlists.tgz',\n",
        "                                    origin='http://www.mythic-ai.com/datasets/wordlists.tgz', untar=True))\n",
        "\n",
        "    img_gen = TextImageGenerator(monogram_file=os.path.join(fdir, 'wordlist_mono_clean.txt'),\n",
        "                                 bigram_file=os.path.join(fdir, 'wordlist_bi_clean.txt'),\n",
        "                                 minibatch_size=minibatch_size,\n",
        "                                 img_w=img_w,\n",
        "                                 img_h=img_h,\n",
        "                                 downsample_factor=(pool_size ** 2),\n",
        "                                 val_split=words_per_epoch - val_words\n",
        "                                 )\n",
        "    act = 'relu'\n",
        "    input_data = Input(name='the_input', shape=input_shape, dtype='float32')\n",
        "    inner = Conv2D(conv_filters, kernel_size, padding='same',\n",
        "                   activation=act, kernel_initializer='he_normal',\n",
        "                   name='conv1')(input_data)\n",
        "    inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max1')(inner)\n",
        "    inner = Conv2D(conv_filters, kernel_size, padding='same',\n",
        "                   activation=act, kernel_initializer='he_normal',\n",
        "                   name='conv2')(inner)\n",
        "    inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max2')(inner)\n",
        "\n",
        "    conv_to_rnn_dims = (img_w // (pool_size ** 2), (img_h // (pool_size ** 2)) * conv_filters)\n",
        "    inner = Reshape(target_shape=conv_to_rnn_dims, name='reshape')(inner)\n",
        "\n",
        "    # cuts down input size going into RNN:\n",
        "    inner = Dense(time_dense_size, activation=act, name='dense1')(inner)\n",
        "\n",
        "    # Two layers of bidirectional GRUs\n",
        "    # GRU seems to work as well, if not better than LSTM:\n",
        "    gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(inner)\n",
        "    gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(inner)\n",
        "    gru1_merged = add([gru_1, gru_1b])\n",
        "    gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)\n",
        "    gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)\n",
        "\n",
        "    # transforms RNN output to character activations:\n",
        "    inner = Dense(img_gen.get_output_size(), kernel_initializer='he_normal',\n",
        "                  name='dense2')(concatenate([gru_2, gru_2b]))\n",
        "    y_pred = Activation('softmax', name='softmax')(inner)\n",
        "    Model(inputs=input_data, outputs=y_pred).summary()\n",
        "\n",
        "    labels = Input(name='the_labels', shape=[img_gen.absolute_max_string_len], dtype='float32')\n",
        "    input_length = Input(name='input_length', shape=[1], dtype='int64')\n",
        "    label_length = Input(name='label_length', shape=[1], dtype='int64')\n",
        "    # Keras doesn't currently support loss funcs with extra parameters\n",
        "    # so CTC loss is implemented in a lambda layer\n",
        "    loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])\n",
        "\n",
        "    # clipnorm seems to speeds up convergence\n",
        "    sgd = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)\n",
        "\n",
        "    model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)\n",
        "\n",
        "    # the loss calc occurs elsewhere, so use a dummy lambda func for the loss\n",
        "    model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd)\n",
        "    if start_epoch > 0:\n",
        "        weight_file = os.path.join(OUTPUT_DIR, os.path.join(run_name, 'weights%02d.h5' % (start_epoch - 1)))\n",
        "        model.load_weights(weight_file)\n",
        "    # captures output of softmax so we can decode the output during visualization\n",
        "    test_func = K.function([input_data], [y_pred])\n",
        "\n",
        "    viz_cb = VizCallback(run_name, test_func, img_gen.next_val())\n",
        "\n",
        "    model.fit_generator(generator=img_gen.next_train(),\n",
        "                        steps_per_epoch=(words_per_epoch - val_words) // minibatch_size,\n",
        "                        epochs=stop_epoch,\n",
        "                        validation_data=img_gen.next_val(),\n",
        "                        validation_steps=val_words // minibatch_size,\n",
        "                        callbacks=[viz_cb, img_gen],\n",
        "                        initial_epoch=start_epoch)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "13kO-MxI7XWb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Start the training\n",
        "It may take an hour, alternative you can just download my pre-trained model weights.\n",
        "```shell\n",
        "! wget https://github.com/Tony607/keras-image-ocr/releases/download/V0.1/weights24.h5\n",
        "```"
      ]
    },
    {
      "metadata": {
        "id": "YRGglF5L5qzn",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 2094
        },
        "outputId": "541e795b-80d1-49e6-9c9f-e2014690f4a7"
      },
      "cell_type": "code",
      "source": [
        "run_name = datetime.datetime.now().strftime('%Y:%m:%d:%H:%M:%S')\n",
        "train(run_name, 0, 20, 128)\n",
        "# increase to wider images and start at epoch 20. The learned weights are reloaded\n",
        "train(run_name, 20, 25, 512)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from http://www.mythic-ai.com/datasets/wordlists.tgz\n",
            "770048/768223 [==============================] - 0s 0us/step\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "the_input (InputLayer)          (None, 128, 64, 1)   0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1 (Conv2D)                  (None, 128, 64, 16)  160         the_input[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max1 (MaxPooling2D)             (None, 64, 32, 16)   0           conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2 (Conv2D)                  (None, 64, 32, 16)   2320        max1[0][0]                       \n",
            "__________________________________________________________________________________________________\n",
            "max2 (MaxPooling2D)             (None, 32, 16, 16)   0           conv2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "reshape (Reshape)               (None, 32, 256)      0           max2[0][0]                       \n",
            "__________________________________________________________________________________________________\n",
            "dense1 (Dense)                  (None, 32, 32)       8224        reshape[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "gru1 (GRU)                      (None, 32, 512)      837120      dense1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "gru1_b (GRU)                    (None, 32, 512)      837120      dense1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 32, 512)      0           gru1[0][0]                       \n",
            "                                                                 gru1_b[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "gru2 (GRU)                      (None, 32, 512)      1574400     add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "gru2_b (GRU)                    (None, 32, 512)      1574400     add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 32, 1024)     0           gru2[0][0]                       \n",
            "                                                                 gru2_b[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "dense2 (Dense)                  (None, 32, 28)       28700       concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "softmax (Activation)            (None, 32, 28)       0           dense2[0][0]                     \n",
            "==================================================================================================\n",
            "Total params: 4,862,444\n",
            "Trainable params: 4,862,444\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "WARNING:tensorflow:Variable *= will be deprecated. Use variable.assign_mul if you want assignment to the variable value or 'x = x * y' if you want a new python Tensor object.\n",
            "Epoch 1/20\n",
            " 21/400 [>.............................] - ETA: 2:59 - loss: 24.5378"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 133s 333ms/step - loss: 12.3487 - val_loss: 10.4443\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 2.754 Mean normalized edit distance: 0.781\n",
            "Epoch 2/20\n",
            "287/400 [====================>.........] - ETA: 33s - loss: 6.3274"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 129s 323ms/step - loss: 5.4518 - val_loss: 3.5311\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 1.062 Mean normalized edit distance: 0.295\n",
            "Epoch 3/20\n",
            "360/400 [==========================>...] - ETA: 11s - loss: 0.8411"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 130s 324ms/step - loss: 0.7553 - val_loss: 0.0340\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.016 Mean normalized edit distance: 0.006\n",
            "Epoch 4/20\n",
            "378/400 [===========================>..] - ETA: 6s - loss: 5.3351"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 129s 323ms/step - loss: 5.0730 - val_loss: 0.9439\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.305 Mean normalized edit distance: 0.088\n",
            "Epoch 5/20\n",
            "384/400 [===========================>..] - ETA: 4s - loss: 0.3049"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 129s 323ms/step - loss: 0.2983 - val_loss: 0.2261\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.051 Mean normalized edit distance: 0.015\n",
            "Epoch 6/20\n",
            "385/400 [===========================>..] - ETA: 4s - loss: 0.1098"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 129s 322ms/step - loss: 0.1104 - val_loss: 0.2191\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.051 Mean normalized edit distance: 0.015\n",
            "Epoch 7/20\n",
            "385/400 [===========================>..] - ETA: 4s - loss: 0.8603"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 129s 322ms/step - loss: 0.8391 - val_loss: 0.4505\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.125 Mean normalized edit distance: 0.033\n",
            "Epoch 8/20\n",
            "385/400 [===========================>..] - ETA: 4s - loss: 0.2658"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 129s 322ms/step - loss: 0.2642 - val_loss: 0.2280\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.078 Mean normalized edit distance: 0.022\n",
            "Epoch 9/20\n",
            "385/400 [===========================>..] - ETA: 4s - loss: 0.1978"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 131s 326ms/step - loss: 0.1949 - val_loss: 0.2185\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.059 Mean normalized edit distance: 0.017\n",
            "Epoch 10/20\n",
            "384/400 [===========================>..] - ETA: 5s - loss: 0.2668"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 138s 344ms/step - loss: 0.2624 - val_loss: 0.3137\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.117 Mean normalized edit distance: 0.033\n",
            "Epoch 11/20\n",
            "384/400 [===========================>..] - ETA: 4s - loss: 0.1606"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 135s 337ms/step - loss: 0.1591 - val_loss: 0.1471\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.043 Mean normalized edit distance: 0.013\n",
            "Epoch 12/20\n",
            "384/400 [===========================>..] - ETA: 4s - loss: 0.1270"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 135s 338ms/step - loss: 0.1245 - val_loss: 0.4319\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.133 Mean normalized edit distance: 0.037\n",
            "Epoch 13/20\n",
            "384/400 [===========================>..] - ETA: 4s - loss: 0.1239"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 136s 340ms/step - loss: 0.1217 - val_loss: 0.1755\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.062 Mean normalized edit distance: 0.018\n",
            "Epoch 14/20\n",
            "384/400 [===========================>..] - ETA: 4s - loss: 0.0550"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 136s 339ms/step - loss: 0.0558 - val_loss: 0.2097\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.074 Mean normalized edit distance: 0.021\n",
            "Epoch 15/20\n",
            "384/400 [===========================>..] - ETA: 4s - loss: 0.0608"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 136s 341ms/step - loss: 0.0608 - val_loss: 0.1681\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.043 Mean normalized edit distance: 0.012\n",
            "Epoch 16/20\n",
            "383/400 [===========================>..] - ETA: 5s - loss: 0.0589"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 136s 341ms/step - loss: 0.0588 - val_loss: 0.1111\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.020 Mean normalized edit distance: 0.005\n",
            "Epoch 17/20\n",
            "383/400 [===========================>..] - ETA: 5s - loss: 0.0369"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 135s 338ms/step - loss: 0.0378 - val_loss: 0.1912\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.043 Mean normalized edit distance: 0.013\n",
            "Epoch 18/20\n",
            "383/400 [===========================>..] - ETA: 5s - loss: 0.0426"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 135s 339ms/step - loss: 0.0472 - val_loss: 0.1881\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.074 Mean normalized edit distance: 0.021\n",
            "Epoch 19/20\n",
            "383/400 [===========================>..] - ETA: 5s - loss: 0.0199"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "400/400 [==============================] - 138s 346ms/step - loss: 0.0189 - val_loss: 0.0734\n",
            "\n",
            "Out of 256 samples:  Mean edit distance: 0.047 Mean normalized edit distance: 0.014\n",
            "Epoch 20/20\n",
            "138/400 [=========>....................] - ETA: 1:23 - loss: -5.7473e-04"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "P4jVRC6J3vUy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Predict\n",
        "Download a pre-trained weights file."
      ]
    },
    {
      "metadata": {
        "id": "1TIu0VHI3ze-",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "db31098f-cc67-4fb4-e0f2-176e9c01b49a",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1526105960543,
          "user_tz": -480,
          "elapsed": 2570,
          "user": {
            "displayName": "Chengwei Zhang",
            "photoUrl": "//lh5.googleusercontent.com/-FK2ckwmh6mM/AAAAAAAAAAI/AAAAAAAAPLw/SX9b1QAzJ5g/s50-c-k-no/photo.jpg",
            "userId": "114808171854651597062"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "!wget https://github.com/Tony607/keras-image-ocr/releases/download/V0.1/weights24.h5"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-05-12 06:19:19--  https://github.com/Tony607/keras-image-ocr/releases/download/V0.1/weights24.h5\r\n",
            "Resolving github.com (github.com)... 192.30.253.112, 192.30.253.113\r\n",
            "Connecting to github.com (github.com)|192.30.253.112|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-production-release-asset-2e65be.s3.amazonaws.com/133029758/81b50290-5551-11e8-98c4-db72d80e52fc?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20180512%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20180512T061919Z&X-Amz-Expires=300&X-Amz-Signature=df16a42200915edf55e271a9c3dcbc59540972c27970b97b199c0afedeae5aaf&X-Amz-SignedHeaders=host&actor_id=0&response-content-disposition=attachment%3B%20filename%3Dweights24.h5&response-content-type=application%2Foctet-stream [following]\n",
            "--2018-05-12 06:19:19--  https://github-production-release-asset-2e65be.s3.amazonaws.com/133029758/81b50290-5551-11e8-98c4-db72d80e52fc?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20180512%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20180512T061919Z&X-Amz-Expires=300&X-Amz-Signature=df16a42200915edf55e271a9c3dcbc59540972c27970b97b199c0afedeae5aaf&X-Amz-SignedHeaders=host&actor_id=0&response-content-disposition=attachment%3B%20filename%3Dweights24.h5&response-content-type=application%2Foctet-stream\n",
            "Resolving github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)... 52.216.16.32\n",
            "Connecting to github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)|52.216.16.32|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 19490408 (19M) [application/octet-stream]\n",
            "Saving to: ‘weights24.h5’\n",
            "\n",
            "weights24.h5        100%[===================>]  18.59M  29.4MB/s    in 0.6s    \n",
            "\n",
            "2018-05-12 06:19:20 (29.4 MB/s) - ‘weights24.h5’ saved [19490408/19490408]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "on6qCFcV5uPx",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "3dcc507c-3baa-4d00-d38f-eb2e65d840b0",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1526105966476,
          "user_tz": -480,
          "elapsed": 5918,
          "user": {
            "displayName": "Chengwei Zhang",
            "photoUrl": "//lh5.googleusercontent.com/-FK2ckwmh6mM/AAAAAAAAAAI/AAAAAAAAPLw/SX9b1QAzJ5g/s50-c-k-no/photo.jpg",
            "userId": "114808171854651597062"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "weight_file = './weights24.h5'\n",
        "img_w = 128\n",
        "# Input Parameters\n",
        "img_h = 64\n",
        "words_per_epoch = 16000\n",
        "val_split = 0.2\n",
        "val_words = int(words_per_epoch * (val_split))\n",
        "\n",
        "# Network parameters\n",
        "conv_filters = 16\n",
        "kernel_size = (3, 3)\n",
        "pool_size = 2\n",
        "time_dense_size = 32\n",
        "rnn_size = 512\n",
        "minibatch_size = 32\n",
        "\n",
        "if K.image_data_format() == 'channels_first':\n",
        "    input_shape = (1, img_w, img_h)\n",
        "else:\n",
        "    input_shape = (img_w, img_h, 1)\n",
        "\n",
        "fdir = os.path.dirname(get_file('wordlists.tgz',\n",
        "                                origin='http://www.mythic-ai.com/datasets/wordlists.tgz', untar=True))\n",
        "\n",
        "img_gen = TextImageGenerator(monogram_file=os.path.join(fdir, 'wordlist_mono_clean.txt'),\n",
        "                             bigram_file=os.path.join(fdir, 'wordlist_bi_clean.txt'),\n",
        "                             minibatch_size=minibatch_size,\n",
        "                             img_w=img_w,\n",
        "                             img_h=img_h,\n",
        "                             downsample_factor=(pool_size ** 2),\n",
        "                             val_split=words_per_epoch - val_words\n",
        "                             )\n",
        "act = 'relu'\n",
        "input_data = Input(name='the_input', shape=input_shape, dtype='float32')\n",
        "inner = Conv2D(conv_filters, kernel_size, padding='same',\n",
        "               activation=act, kernel_initializer='he_normal',\n",
        "               name='conv1')(input_data)\n",
        "inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max1')(inner)\n",
        "inner = Conv2D(conv_filters, kernel_size, padding='same',\n",
        "               activation=act, kernel_initializer='he_normal',\n",
        "               name='conv2')(inner)\n",
        "inner = MaxPooling2D(pool_size=(pool_size, pool_size), name='max2')(inner)\n",
        "\n",
        "conv_to_rnn_dims = (img_w // (pool_size ** 2), (img_h // (pool_size ** 2)) * conv_filters)\n",
        "inner = Reshape(target_shape=conv_to_rnn_dims, name='reshape')(inner)\n",
        "\n",
        "# cuts down input size going into RNN:\n",
        "inner = Dense(time_dense_size, activation=act, name='dense1')(inner)\n",
        "\n",
        "# Two layers of bidirectional GRUs\n",
        "# GRU seems to work as well, if not better than LSTM:\n",
        "gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(inner)\n",
        "gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(inner)\n",
        "gru1_merged = add([gru_1, gru_1b])\n",
        "gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)\n",
        "gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)\n",
        "\n",
        "# transforms RNN output to character activations:\n",
        "inner = Dense(img_gen.get_output_size(), kernel_initializer='he_normal',\n",
        "              name='dense2')(concatenate([gru_2, gru_2b]))\n",
        "y_pred = Activation('softmax', name='softmax')(inner)\n",
        "Model(inputs=input_data, outputs=y_pred).summary()\n",
        "\n",
        "labels = Input(name='the_labels', shape=[img_gen.absolute_max_string_len], dtype='float32')\n",
        "input_length = Input(name='input_length', shape=[1], dtype='int64')\n",
        "label_length = Input(name='label_length', shape=[1], dtype='int64')\n",
        "# Keras doesn't currently support loss funcs with extra parameters\n",
        "# so CTC loss is implemented in a lambda layer\n",
        "loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])\n",
        "\n",
        "# clipnorm seems to speeds up convergence\n",
        "sgd = SGD(lr=0.02, decay=1e-6, momentum=0.9, nesterov=True, clipnorm=5)\n",
        "\n",
        "model = Model(inputs=[input_data, labels, input_length, label_length], outputs=loss_out)\n",
        "\n",
        "# the loss calc occurs elsewhere, so use a dummy lambda func for the loss\n",
        "model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=sgd)\n",
        "model.load_weights(weight_file)\n",
        "# captures output of softmax so we can decode the output during visualization\n",
        "test_func = K.function([input_data], [y_pred])\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from http://www.mythic-ai.com/datasets/wordlists.tgz\n",
            "770048/768223 [==============================] - 0s 0us/step\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "the_input (InputLayer)          (None, 128, 64, 1)   0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1 (Conv2D)                  (None, 128, 64, 16)  160         the_input[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max1 (MaxPooling2D)             (None, 64, 32, 16)   0           conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "conv2 (Conv2D)                  (None, 64, 32, 16)   2320        max1[0][0]                       \n",
            "__________________________________________________________________________________________________\n",
            "max2 (MaxPooling2D)             (None, 32, 16, 16)   0           conv2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "reshape (Reshape)               (None, 32, 256)      0           max2[0][0]                       \n",
            "__________________________________________________________________________________________________\n",
            "dense1 (Dense)                  (None, 32, 32)       8224        reshape[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "gru1 (GRU)                      (None, 32, 512)      837120      dense1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "gru1_b (GRU)                    (None, 32, 512)      837120      dense1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 32, 512)      0           gru1[0][0]                       \n",
            "                                                                 gru1_b[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "gru2 (GRU)                      (None, 32, 512)      1574400     add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "gru2_b (GRU)                    (None, 32, 512)      1574400     add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 32, 1024)     0           gru2[0][0]                       \n",
            "                                                                 gru2_b[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "dense2 (Dense)                  (None, 32, 28)       28700       concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "softmax (Activation)            (None, 32, 28)       0           dense2[0][0]                     \n",
            "==================================================================================================\n",
            "Total params: 4,862,444\n",
            "Trainable params: 4,862,444\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_f_3wfZr36dq",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 1549
        },
        "outputId": "c360da6c-190e-4a05-fa1c-46f8018161b9",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1526105969268,
          "user_tz": -480,
          "elapsed": 2778,
          "user": {
            "displayName": "Chengwei Zhang",
            "photoUrl": "//lh5.googleusercontent.com/-FK2ckwmh6mM/AAAAAAAAAAI/AAAAAAAAPLw/SX9b1QAzJ5g/s50-c-k-no/photo.jpg",
            "userId": "114808171854651597062"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "from keras.utils import plot_model\n",
        "plot_model(model, to_file='model.png', show_shapes=True)\n",
        "from IPython.display import Image\n",
        "Image(filename='model.png')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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X///beuj/KSTXpS\nU1ONn8vIcQoKCmbPnu3j42NnZ6dWq3v06DF16tTs7Gy90eQLS1y/fr226XSSkpJM8s5rqnEAiyP/\n62mb+V+SJPmcBj3r16/X63b8+PHQ0FBXV1e1Wu3j4/PSSy8p3yNkTZ+TJT45AzUYmc2UWnFuNz7b\ntNn0bnzqNmG+bcCzFGhlGvZZqBWna+MzDOm6zkNUM8fyFQZujn+l0JSa/vkmv/M15Yz1YuQH4sLC\nws6dO0+ZMqUJQmqkv/76S6PRxMTEtLK5JElKT09XqVQffvihMZ0pQgB6yP96yP+NZJGcLPHJGaih\nib/ebea53fhs0zbTe71SN0UIwISa/v/KZp6ujc8wpOs61cyxLMcEAC2Vq6vrvn37du3atXr1akvH\nYogkSbGxsS4uLosXL25Ncwkhzp07FxERER8fP378+CaYDgBk5P+bIicDaOnaYHondQNoiUjXDUAR\nApaXlJR05513ajQaeU32U6dOWTqiRjl79qxKpdK7bi1gJv7+/idPnjx06FBxcbGlY6lVTk7OuXPn\njhw54uXl1ZrmEkKsW7du6dKlS5cubYK5TGXPnj26a2Aor+9qQgkJCfL4Xbp0Md9dzDRITU5OTsoL\nhyQkJJhw8EZqzrHB3Mj/NbXEnNzMmSmvNoYJ38WacwptzrHB3Npaem/OqbulfKYlV1tKc44NTYB0\nXW+NPxcDrVKTnVT+9ddfq1Sq2bNnl5SU/Pbbb126dPnpp5+aYF7ziY+Pl19cp0+fNuGwJSUlt956\nq27FN7NqyrlkTbmIwWuvvabMgfKyes2N4NTgVqr5LMcUHh4uhCgvL298MLVpwJm2Jjk51xxn+P7w\nww9CiPDwcNMOaxLNOTZjkP/1kP+bUtMvx9T0H7FMohmunGCqd7HmnEKbc2zGaLJs1iJyOwtpmlDT\nL8fUUj7TkqstojnHZoymzE4tIl2zwKYJ1cyxnAkBC9u5c6ckSdOnT3dycurRo8eFCxf69Onj5OQU\nHBxs6dAaorq6evPmzf7+/kKI999/v2GD3HT3JUmqrq6urq5ubIiWm6uZmDVrljIJLlmyxNIRAWbU\nctMp9PBQNh75HxZn8Y9YLS6TtLiAG6At7KNZkdsBi2sLeawt7KO5ka5hY+kA0NZduHBBCKHVai0d\niGl8+umnNjY27733XmBg4JYtW5YtW2ZjY5pXmbOzc1ZWlkmGalZzAQAAtBF8xAIAAEDbxJkQsLAb\nN25YOgRT2rhx42OPPdavX7877rgjJyfn4MGDlo4IAAAAAAAAACyGIgQapaKiYuHChT4+Pg4ODu3a\ntRs9evTHH3+srCvk5+fPnDmzR48earXa3d195MiRX3zxhbxJvlTR3r17hRDyVan79+8vX1KprKws\nJSVFvraPfCaB8rpGf/zxR1RUlLOzs1arnTRp0l9//XX+/PnRo0c7Ozt37NjxySefLCkp0QVQVVWV\nlJQ0bNgwLy8vjUbTt2/flStX6s6CDw4O1g07ceJEIcTQoUN1LYWFhfU6GgUFBfv27Xv00UeFEI8/\n/rgQYuPGjTW76Y6JnZ1dly5dhg4dumnTpvLycvHfK0oZ3v1r164VFhYqL38kn8VWVVWla3nooYfq\n3Hcj5zL+oZSdP38+KirKzc1Nq9WGhYXxcz/AUmp7jStlZ2cbeMHm5ubGxsZ6e3ur1er27dtHRESk\np6c3OB7DGUnpzJkzo0aNcnV1dXBwGDx4cEpKSmOiqvN9qmGMyXvKiwSmpaUNGTLE2dlZb6eWLFki\n99Gd33348GG5xcPDQzmO4YfSMAMH38g3FGHwyCuPxq+//jpu3DitVivfzMvLa9SBBlqRmh+xWlYm\nUTIyIRj+WHjmzJkHHnhAzvZ33333/v37dZ/DY2JiGv8u1jAt60EhvQNNz1KfaRuAXE2uJlejeTHl\nJSfQihh5MZaYmBhXV9dPP/3077//zs7OnjVrlhDiiy++kLdeuXKle/fuHTp02LdvX1FR0a+//hoR\nEaFSqdavX68b4aaXKnJ0dLzvvvtqTid3joiIOHnyZGlp6ebNm4UQI0eODA8P/+GHH0pKStauXSuE\nmDFjhu4u+/btE0K88sorBQUFubm5b7/9tpWVlXIpuvT0dEdHRz8/v9LSUkmSrl27ds8993z44YfK\neQcPHtyuXbvU1FTDR+Odd94ZPHiw/Hdubq6tra2NjU1OTo6yj3xMvLy89u3bV1xcnJ2dvXjxYiHE\nm2++aeTu647ViBEjrKysfvvtN2WfoKCg7du3G7nvxs9l/EMZHh5+/Pjx0tLSzz77TKPRBAYGGj5o\nMi7+o0dwYdJWqukvTG34Na57wR45csTFxUX5gr18+fI//vGPDh06HDhwoKSk5NSpUwMHDrS3tz9+\n/LiRQepdHM+YjOTn5+fq6jp48OCvv/66pKQkLS3tjjvuUKvVx44dMz4qvXkNv09JxmX42q44Z0ze\n8/Pzc3R0DAoKkvvU3CnpZg9TQECAVqtVttT2UBpzNbw6D35oaOhN31C2bdsm/23MkZePxsCBA7/4\n4ouysrITJ05YW1vn5uYaCEwi/9dA/m9KTX9haulmH32bQyYxTC+vGp8QDOzU2bNn3dzcOnfu/Omn\nn8qDDB06tH379nZ2dsYEXOe7mER6lyTJoumdbKbEhalNyOIXprbUZ9r6xkmu1h0WcrUBZCc9/G9i\nQjVzLE813JyRL7zu3bvfe++9ypZevXrpvtx57LHHhBDKL/SvXbvWqVMnjUaTnZ0ttzSgCHHgwAFd\ni6+vrxDiyy+/VIbUu3dv3c19+/YNGjRIOcjEiRNtbW2Liop0LcnJyXJto7q6+tFHH507d67evAMH\nDnR3d6/zq7e77rpr8+bNuptjx44VQiQkJCj7yMdE73U4YsSIBhQhPv/8cyHEs88+q+vw9ddfd+vW\nrbKy0vh9N3Iu4x/Kffv26frIRfs63/YkEn0N/NvWWjW3IoTyBfvwww8rX7DySV26D76SJF25csXO\nzi4gIMDIIGv+w1ZnRvLz8xNCKP8J+fHHH4UQfn5+xkelN6/h9ynJuAxv+D8fw3lP3qkffvihtp2S\nmuQ/H8MH/5NPPqn5htK5c+fr16/LN4058vLROHjwoIFIaiL/6yH/N6VmVYSwbCYxTC+vGp8QDOxU\nZGSkEGLXrl26DlevXnVwcKjXF1sG3sUk0rskSRZN72QzJb7mM6HmUISwyGfa+sZJrpaRqw0jO+nh\nfxMToggBYxn5wnvmmWeEEE8++WRqampVVZXeVldXVyFEcXGxsnHSpElCiA8++EC+2YAihPLcgmHD\nhgkhysrKdC3BwcHOzs4GYn7ttdeEEHpvcvPmzRNC3HvvvWFhYTdu3DC0z7XIyMhwdnZWRvLxxx8L\nIXx9fZXdbnpM9BhZGJAkyd/f38HBIS8vT9fhjTfeMDByzX03ci7jH0pdTUKSpBkzZgghMjIyDIQk\nkz/cAG1Ena+IOpmqCKF8wc6ePVsoXrCurq5WVlbK/6YkSbrrrruEEBcuXDBm6jr/caqZkfz8/Ozt\n7aurq5XdOnXqJIS4fPmykVHpzWv4fcpIhv/zMZz35J9f6d1RuVOS+f/zqanmwe/bt6/eG8qrr76q\n22rMkZePhm4EI5H/YXH1esYaeCY3sghh2UximF5eNT4hGNgpZ2dnIURJSYneIPX6YsvAu5iRSO/m\nS+9N/UpGW2LZIkRNTfOZtr5xkqtl5GrD5P8rATPRy7ENXwkUEEKsXr06KCjogw8+GDJkiBBiwIAB\nU6ZMkc8AqKioKCoqsre3l9+3dDp06CCEyM7ObvCkLi4uur+trKysra0dHBx0LdbW1soFGYuKil5/\n/fXdu3dfvHhReY2Hv//+Wznm4sWLP//88+PHj3/wwQdWVg25VsrGjRtLSkocHR312k+fPv3tt9/e\nfffdovZj0mDPP//8xIkT16xZs2DBgszMzK+++mrLli26rUbue53q9VDK5QqZWq0WQtx0fcyagoKC\n5E8MEEKMGzduxowZQUFBlg4EJpaamvrmm29aOor/o3zByqlPfsHKr3q9Djpnz57t0qVLfecyMiPJ\nC5gqWzw9PS9fvnz16tV27do1ICoD71OmUmfec3Nz07uLbqc6duxowkhqY8zBj4uLe+KJJ3RvKEeP\nHn3//fflTfV6PtR8H6wT+V+J/N+UmlVObv6ZRFavhFDbTlVUVJSUlNjb2zs5OSnv7u7uXq9gansX\nM5Xm/6A08/RONtORs418/j0ayeJ521KfaeuFXK3sQK6uE9lJR84w/G9iEuPGjdNroQiBRlGpVJMm\nTZo0aVJlZeWxY8cSEhIiIiJef/31mTNn2tnZubq6FhUVlZSUKL+8zsnJEUJ4eXkZHtZUEY4ePfo/\n//nPypUrJ0yY4OHhoVKp3nrrLfmiEcpux44dKyoq6tu377PPPuvn5yefsme8ysrKbdu2paSk3Hvv\nvcr2GTNmvPXWW++//75chKjtmOgxfvejoqLi4+NXrVr1wgsvvP76608++aRyWGP23Zi5GvNQGq9L\nly78Hlapf//+HJDWR2ryHwY2LJ3a2dm5ubmVlpaWl5c35sqlSkZmY/kTttLVq1eFEJ6eng2LysD7\nlEn2yxj5+fmSJCkfC91OyTetrKyuX7+uvIvy/xNZY94ZjTn40dHRc+fO1b2hPProo7r/MM3xfFAi\n/+sh/zeZps/JjWHxTCIzSUKws7NzdnYuKSkpLS1Vfrcl75GSCf8pMAeLPyjNPL2TzXTkR4SjYRI7\nd+60bACW+kxbL+RqJXJ1nchOOnKG4YCYSUN+8Q3ouLm5nTlzRghha2s7bNiwPXv2qFSqAwcOyFvl\nn5rqbgohKioqjhw5otFoQkNDDQzr4OCgew/o3bv3e++917Dwbty4kZKS4uXlFRsb2759e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AepSampqsrCz2JQ53797NyckhzWzhgO+4AABoMhQhAAAA4P/DUk4AnY1ZGWb79u0XL14c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\nJkYVMUJHTZs2LSc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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "YrSqXKIY4WRU",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "model_p = Model(inputs=input_data, outputs=y_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SDkiHH8_4aVJ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "def decode_predict_ctc(out, top_paths = 1):\n",
        "    results = []\n",
        "    beam_width = 5\n",
        "    if beam_width < top_paths:\n",
        "      beam_width = top_paths\n",
        "    for i in range(top_paths):\n",
        "      lables = K.get_value(K.ctc_decode(out, input_length=np.ones(out.shape[0])*out.shape[1],\n",
        "                           greedy=False, beam_width=beam_width, top_paths=top_paths)[0][i])[0]\n",
        "      text = labels_to_text(lables)\n",
        "      results.append(text)\n",
        "    return results\n",
        "  \n",
        "def predit_a_image(a, top_paths = 1):\n",
        "  c = np.expand_dims(a.T, axis=0)\n",
        "  net_out_value = model_p.predict(c)\n",
        "  top_pred_texts = decode_predict_ctc(net_out_value, top_paths)\n",
        "  return top_pred_texts\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gVH2yF7DAm10",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "8e483e0f-87a3-4302-bb83-4e28497d135b",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1526107425874,
          "user_tz": -480,
          "elapsed": 1118,
          "user": {
            "displayName": "Chengwei Zhang",
            "photoUrl": "//lh5.googleusercontent.com/-FK2ckwmh6mM/AAAAAAAAAAI/AAAAAAAAPLw/SX9b1QAzJ5g/s50-c-k-no/photo.jpg",
            "userId": "114808171854651597062"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "h = 64\n",
        "w = 128\n",
        "a = paint_text('a game',h = h, w = w)\n",
        "b = a.reshape((h, w))\n",
        "plt.imshow(b, cmap='Greys_r')\n",
        "plt.show()\n",
        "\n",
        "c = np.expand_dims(a.T, axis=0)\n",
        "\n",
        "net_out_value = model_p.predict(c)\n",
        "pred_texts = decode_predict_ctc(net_out_value)\n",
        "pred_texts"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W2NSpUytbSyGEEGI/Ykiz8+zZs+0LX/iCtbW1WV9fn9100032+7//+zZv3jzr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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3a4938e710>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['a game']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "Zzahy6G-HXy4",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2a92f768-1cd5-4456-8994-073886b1205f",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1526107438636,
          "user_tz": -480,
          "elapsed": 625,
          "user": {
            "displayName": "Chengwei Zhang",
            "photoUrl": "//lh5.googleusercontent.com/-FK2ckwmh6mM/AAAAAAAAAAI/AAAAAAAAPLw/SX9b1QAzJ5g/s50-c-k-no/photo.jpg",
            "userId": "114808171854651597062"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "predit_a_image(a, top_paths = 3)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['a game', 'za game', 'fa game']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "RPuabD6PAqFR",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "a45dfe84-156d-4290-9cfd-601a24b27684",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1526105974272,
          "user_tz": -480,
          "elapsed": 616,
          "user": {
            "displayName": "Chengwei Zhang",
            "photoUrl": "//lh5.googleusercontent.com/-FK2ckwmh6mM/AAAAAAAAAAI/AAAAAAAAPLw/SX9b1QAzJ5g/s50-c-k-no/photo.jpg",
            "userId": "114808171854651597062"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(net_out_value[0].T, cmap='binary', interpolation='nearest')\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pz7o1m+qYJKmlpUWVlZU6evSoXrx4kYHJnMvJyZHX65Uktba2auPGjVm/TtLUx5WTk5PV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\nYQcAYwg7ABhD2AHAGMIOAMb8H4ZgmHQE93DlAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3a49f00e48>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "9M94ioRQA217",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3b1d8af0-4768-4954-bba3-fdb81df9e9c4",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1526105974956,
          "user_tz": -480,
          "elapsed": 645,
          "user": {
            "displayName": "Chengwei Zhang",
            "photoUrl": "//lh5.googleusercontent.com/-FK2ckwmh6mM/AAAAAAAAAAI/AAAAAAAAPLw/SX9b1QAzJ5g/s50-c-k-no/photo.jpg",
            "userId": "114808171854651597062"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "K.get_value(K.ctc_decode(net_out_value, input_length=np.ones(net_out_value.shape[0])*net_out_value.shape[1],\n",
        "                         greedy=False, beam_width=3, top_paths=3)[0][0])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0, 26,  6,  0, 12,  4]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "VeB1FAyVIpl_",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "bec04e29-83e9-4614-8c31-700a16b7b898",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1526106981099,
          "user_tz": -480,
          "elapsed": 622,
          "user": {
            "displayName": "Chengwei Zhang",
            "photoUrl": "//lh5.googleusercontent.com/-FK2ckwmh6mM/AAAAAAAAAAI/AAAAAAAAPLw/SX9b1QAzJ5g/s50-c-k-no/photo.jpg",
            "userId": "114808171854651597062"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "K.ctc_decode(net_out_value, input_length=np.ones(net_out_value.shape[0])*net_out_value.shape[1],\n",
        "                         greedy=False, beam_width=5, top_paths=3)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([<tf.Tensor 'SparseToDense_25:0' shape=(?, ?) dtype=int64>,\n",
              "  <tf.Tensor 'SparseToDense_26:0' shape=(?, ?) dtype=int64>,\n",
              "  <tf.Tensor 'SparseToDense_27:0' shape=(?, ?) dtype=int64>,\n",
              "  <tf.Tensor 'SparseToDense_28:0' shape=(?, ?) dtype=int64>,\n",
              "  <tf.Tensor 'SparseToDense_29:0' shape=(?, ?) dtype=int64>,\n",
              "  <tf.Tensor 'SparseToDense_30:0' shape=(?, ?) dtype=int64>],\n",
              " <tf.Tensor 'CTCBeamSearchDecoder_8:18' shape=(1, 6) dtype=float32>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "b0NDGYZwKElX",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e8395f8a-b8c9-46dc-b174-dba3de6fe884",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1526105901853,
          "user_tz": -480,
          "elapsed": 1681,
          "user": {
            "displayName": "Chengwei Zhang",
            "photoUrl": "//lh5.googleusercontent.com/-FK2ckwmh6mM/AAAAAAAAAAI/AAAAAAAAPLw/SX9b1QAzJ5g/s50-c-k-no/photo.jpg",
            "userId": "114808171854651597062"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "datalab\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Qe-RceTPK-yJ",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7081439c-61f3-4e2d-f293-5c68721d6496",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1526106995201,
          "user_tz": -480,
          "elapsed": 1392,
          "user": {
            "displayName": "Chengwei Zhang",
            "photoUrl": "//lh5.googleusercontent.com/-FK2ckwmh6mM/AAAAAAAAAAI/AAAAAAAAPLw/SX9b1QAzJ5g/s50-c-k-no/photo.jpg",
            "userId": "114808171854651597062"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "K.get_value(K.ctc_decode(net_out_value, input_length=np.ones(net_out_value.shape[0])*net_out_value.shape[1],\n",
        "                         greedy=False, beam_width=3, top_paths=3)[0][0])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0, 26,  6,  0, 12,  4]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "qz7VeMhxC7ID",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "K.ctc_decode?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aCAJ0joMDJWO",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}