Text_Detection/2_Tesseract_Failure_Cases.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "2_Tesseract_Failure_Cases.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JeanJulesBigeard/Getting-started-with-OpenCV/blob/master/Text_Detection/2_Tesseract_Failure_Cases.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "znK35cEV83Et",
        "colab_type": "text"
      },
      "source": [
        "# <font color=\"blue\">Failure Cases in Tesseract OCR</font>\n",
        "In this notebook, we will see some instances where Tesseract does not work as expected and provide logical reasons for them. We will see some examples from scanned documents as well as natural camera images.\n",
        "\n",
        "We will discuss how to improve the OCR output in the next notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONKd4B6G-f8i",
        "colab_type": "text"
      },
      "source": [
        "# <font color=\"blue\">Install Tesseract</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ip5LAcpMVpoz",
        "colab_type": "code",
        "outputId": "7541fff1-c59c-414d-9bde-1399a0f027fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "!apt install libtesseract-dev tesseract-ocr > /dev/null\n",
        "!pip install pytesseract > /dev/null"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "WARNING: apt does not have a stable CLI interface. Use with caution in scripts.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhDZ1eVK_Evf",
        "colab_type": "text"
      },
      "source": [
        "# <font color=\"blue\">Import Libraries </font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VR5F102-Vt_v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pytesseract\n",
        "import cv2\n",
        "import glob\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from IPython.display import Image"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4YqkRIefLKI",
        "colab_type": "text"
      },
      "source": [
        "# <font color=\"blue\">Failure Cases Categories </font>\n",
        "Even though failure cannot be always attributed to one specific reasons, We have listed a few major reasons for failure of OCR using Tesseract and in general. They are:\n",
        "1. **Cluttered Background** - The text might not be visibly clear or it might appear camouflaged with the background.\n",
        "1. **Small Text** - The text might be too small to be detected. \n",
        "1. **Rotation or Perspective Distortion** - The text might be rotated in the image or the image itself might be distorted.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XAOXdTpZvOEW",
        "colab_type": "text"
      },
      "source": [
        "# <font color=\"blue\">Test Image 1: Image of a Book</font>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m5YjoL6qvOWh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget https://www.dropbox.com/s/uwrdek4jjac4ysz/book2.jpg?dl=1 -O book2.jpg --quiet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6kCyyYfWvllI",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Downloaded Image</font>\n",
        "\n",
        "<img src=\"https://www.dropbox.com/s/uwrdek4jjac4ysz/book2.jpg?dl=1\" width=500>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LZ1HQqsbBK8M",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Output</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-r8x0wZYwgws",
        "colab_type": "code",
        "outputId": "8b11d5a5-5f11-4e39-cdbe-085d18c5dd0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "book2_text = pytesseract.image_to_string('book2.jpg')\n",
        "print(book2_text)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Impact of the Highly Improbable\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bgFJkHHsZPVp",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Observation </font>\n",
        "We saw this example in the last notebook. The major reason is the relatively cluttered background and low contrast of the white text on orange background. Another issue with these kind of images is the variability of text size. **\"Black\"** is written in extra-large size whereas **\"The impact of\"** is normal."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B-zgzR5sxjym",
        "colab_type": "text"
      },
      "source": [
        "# <font color=\"blue\">Test Image 2: Driving License </font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NlIlSYkVLAQL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget https://www.dropbox.com/s/rdaha84n8jo3bmw/dl.jpg?dl=0 -O dl.jpg --quiet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPrrWdGYBh8n",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Downloaded Image</font>\n",
        "![](https://www.dropbox.com/s/rdaha84n8jo3bmw/dl.jpg?dl=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lB1AKcTrBPJW",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Output</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UmQS6qAGK_cU",
        "colab_type": "code",
        "outputId": "a344ca70-1bd9-4a52-cbd3-74f82ed1437e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        }
      },
      "source": [
        "dl_text = pytesseract.image_to_string('dl.jpg')\n",
        "print(dl_text)\n",
        "Image(\"dl.jpg\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DRIVER LICENSE\n",
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            "= a\n",
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            "123 486 789 Sein States,\n",
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            "\n",
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            "\n",
            "  \n",
            "\n",
            "IVANYWHERE STREET\n",
            "ITY, DL\n",
            "\n",
            "500 asosse7eo 67890-1071 ah NA Yuare\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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5pMFEl7b/APUY9hUrUPaFFUo5P8JqYrbD2ObGVSIu9HDTf/dUOVVLXs/uG/8AuqGzx/jW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\nhQAKFChQB//Z\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXrwIR4poQlp",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Observation </font>\n",
        "- It is unable to detect small text( Date of Expiry, Address, etc.)\n",
        "- Same issue with cluttered background ( New York on Top Left )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RB_VxB28M3N0",
        "colab_type": "text"
      },
      "source": [
        "# <font color=\"blue\">Test Image 3: License Plate</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vhv0FqVK2yfj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget \"https://www.dropbox.com/s/xz24vxrp4uvvnri/license_plate.jpg?dl=0\" -O lp1.jpg --quiet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mw122YmMmjA",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Downloaded Image</font>\n",
        "<img src=\"https://www.dropbox.com/s/xz24vxrp4uvvnri/license_plate.jpg?dl=1\" width=500>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pwhw8TAjEQqJ",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Output</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1pbRHpQ2-6H",
        "colab_type": "code",
        "outputId": "65060329-700e-4ba3-d87b-e6477616920e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "lp_text = pytesseract.image_to_string('lp1.jpg')\n",
        "print(lp_text)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tiles)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PZR6S45AM8dV",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Observation </font>\n",
        "Even though the text is very clear to us, Tesseract finds it difficult to recognize. The major issue is with contrast. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiRFxoD_Dy11",
        "colab_type": "text"
      },
      "source": [
        "# <font color=\"blue\">Street Signs</font>\n",
        "Street signs are one of the most difficult ones to recognize. Let us see some examples."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EJ1HqPpcNLzF",
        "colab_type": "text"
      },
      "source": [
        "# <font color=\"blue\">Test Image 4</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pfkn0xGU8NhT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget https://www.dropbox.com/s/uwlnxiihqgni57o/streetsign1.jpg?dl=0 -O streetsign1.jpg --quiet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uL42MgotyLt6",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Downloaded Image</font>\n",
        "<img src=\"https://www.dropbox.com/s/uwlnxiihqgni57o/streetsign1.jpg?dl=1\" width=500>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "acC4MknhEJvG",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Output</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GB9dyjPO8T6N",
        "colab_type": "code",
        "outputId": "8148c531-1d27-4cbd-e7d2-4868410fbb4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ss1_text = pytesseract.image_to_string('streetsign1.jpg')\n",
        "print(ss1_text)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXhXjB4cNQs8",
        "colab_type": "text"
      },
      "source": [
        "# <font color=\"blue\">Test Image 5</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jCEOYVvT8UOS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget https://www.dropbox.com/s/dbkag5gsicxqoqg/streetsign2.jpg?dl=0 -O streetsign2.jpg --quiet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1GQB-KhMQn1",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Downloaded Image</font>\n",
        "<img src=\"https://www.dropbox.com/s/dbkag5gsicxqoqg/streetsign2.jpg?dl=1\" width=500>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HnrMq987ENh6",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Output</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymF4fo3u9wJy",
        "colab_type": "code",
        "outputId": "3a5db547-fd01-4bce-a31f-e7e2e6e56b64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ss2_text = pytesseract.image_to_string('streetsign2.jpg')\n",
        "print(ss2_text)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E3nyo3JMNVpt",
        "colab_type": "text"
      },
      "source": [
        "# <font color=\"blue\">Test Image 6</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7AF7mi79pDJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget https://www.dropbox.com/s/cgni28zl1k9sesk/streetsign3.jpg?dl=0 -O streetsign3.jpg --quiet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GdeRNQmFMZsb",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Downloaded Image</font>\n",
        "<img src=\"https://www.dropbox.com/s/cgni28zl1k9sesk/streetsign3.jpg?dl=1\" width=500>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzJlqmjWEO6r",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Output</font>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oG_qfwVp_Kmo",
        "colab_type": "code",
        "outputId": "4b3fd507-5e67-4503-f421-be236305d5c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "ss3_text = pytesseract.image_to_string('streetsign3.jpg')\n",
        "print(ss3_text)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3M9_MokcNX2l",
        "colab_type": "text"
      },
      "source": [
        "### <font color=\"green\">Observation</font>\n",
        "In all the above 3 images, there are multiple issues:\n",
        "- text is not aligned. It is either rotated within the plane or into the plane.\n",
        "- Large variation in Text size\n",
        "- Background clutter"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kAeu6BitNsox",
        "colab_type": "text"
      },
      "source": [
        "Next, We will discuss how to overcome some of the limitations of Tesseract by using different techniques."
      ]
    }
  ]
}