easy_OCR.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bSwRcnVLKmIK",
        "outputId": "41052f28-d62e-4155-dd88-746166aa36e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: easyocr in /usr/local/lib/python3.10/dist-packages (1.7.1)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from easyocr) (2.1.0+cu118)\n",
            "Requirement already satisfied: torchvision>=0.5 in /usr/local/lib/python3.10/dist-packages (from easyocr) (0.16.0+cu118)\n",
            "Requirement already satisfied: opencv-python-headless in /usr/local/lib/python3.10/dist-packages (from easyocr) (4.8.1.78)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from easyocr) (1.11.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from easyocr) (1.23.5)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from easyocr) (9.4.0)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.10/dist-packages (from easyocr) (0.19.3)\n",
            "Requirement already satisfied: python-bidi in /usr/local/lib/python3.10/dist-packages (from easyocr) (0.4.2)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from easyocr) (6.0.1)\n",
            "Requirement already satisfied: Shapely in /usr/local/lib/python3.10/dist-packages (from easyocr) (2.0.2)\n",
            "Requirement already satisfied: pyclipper in /usr/local/lib/python3.10/dist-packages (from easyocr) (1.3.0.post5)\n",
            "Requirement already satisfied: ninja in /usr/local/lib/python3.10/dist-packages (from easyocr) (1.11.1.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision>=0.5->easyocr) (2.31.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->easyocr) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch->easyocr) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->easyocr) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->easyocr) (3.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->easyocr) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->easyocr) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch->easyocr) (2.1.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from python-bidi->easyocr) (1.16.0)\n",
            "Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->easyocr) (2.31.6)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.10/dist-packages (from scikit-image->easyocr) (2023.9.26)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->easyocr) (1.4.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image->easyocr) (23.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->easyocr) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision>=0.5->easyocr) (3.3.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision>=0.5->easyocr) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision>=0.5->easyocr) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision>=0.5->easyocr) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->easyocr) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install easyocr"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import easyocr\n",
        "from IPython.display import Image"
      ],
      "metadata": {
        "id": "2s-68iCPKv1K"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Image(\"/content/scanned_img_3.jpg\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "id": "LHYl-5WILRBB",
        "outputId": "f86b5e5e-4676-4807-9d70-666d8dfdb254"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reader = easyocr.Reader(['en']) #englsh txt so en"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YElggU6_LxjR",
        "outputId": "49cf6075-f3fd-4835-9cd0-6421835c8f64"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:easyocr.easyocr:Neither CUDA nor MPS are available - defaulting to CPU. Note: This module is much faster with a GPU.\n",
            "WARNING:easyocr.easyocr:Downloading detection model, please wait. This may take several minutes depending upon your network connection.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress: |██████████████████████████████████████████████████| 100.0% Complete"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:easyocr.easyocr:Downloading recognition model, please wait. This may take several minutes depending upon your network connection.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Progress: |██████████████████████████████████████████████████| 100.0% Complete"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = reader.readtext('/content/scanned_img_3.jpg')"
      ],
      "metadata": {
        "id": "KAYExvYOL9My"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPJkfBGRMPjJ",
        "outputId": "f60efd05-392f-4a9b-a796-f2e91c5b9c27"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[([[0, 8], [148, 8], [148, 47], [0, 47]], 'KA 64 N 06921', 0.2252066625844261)]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cord = output[-1][0]"
      ],
      "metadata": {
        "id": "DH4WvwRvMQzU"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cord"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "soro0D1fSUqu",
        "outputId": "007fa617-a3f8-4876-e11b-9e8d452ade44"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0, 8], [148, 8], [148, 47], [0, 47]]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = list(zip(*cord))\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjJT3nZoSVQn",
        "outputId": "1018ddd4-3b04-4599-e8ee-c6cec5176380"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0, 148, 148, 0), (8, 8, 47, 47)]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min(a[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Hyi6zhtSp1l",
        "outputId": "37c6c36c-fd06-4c1c-ba58-e106e0ccd89c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "min(a[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XDSNU0fISv4K",
        "outputId": "3f8d7c49-0cdc-4479-9222-52888163f42a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(a[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NzjzOSH_S0ww",
        "outputId": "eb5d3ddb-e11a-4ac5-c76c-5bf848ef1095"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "148"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(a[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZicV9nPtS2XL",
        "outputId": "943463ea-aeaa-40b9-c5b1-18bf64f633f9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "47"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_min, y_min = [int(min(idx)) for idx in zip(*cord)]"
      ],
      "metadata": {
        "id": "mfx2LagSS7D0"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_min, y_min"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GBTMxz3NS_v1",
        "outputId": "2b7b11eb-a7ea-43ae-bdad-6b4da5b99f02"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_max, y_max = [int(max(idx)) for idx in zip(*cord)]"
      ],
      "metadata": {
        "id": "1qWi89O4TCte"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_max, y_max"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3n5hAhUgTFjs",
        "outputId": "0c1f4705-2ab2-4c66-da10-f36a494fe9b3"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(148, 47)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pylab import rcParams\n",
        "rcParams['figure.figsize'] = 20, 30"
      ],
      "metadata": {
        "id": "T_6F4IzITHN9"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image = cv2.imread('/content/scanned_img_2.jpg')\n",
        "cv2.rectangle(image,(x_min,y_min),(x_max,y_max),(0,0,255),2)\n",
        "plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "id": "KjGW8ireTMoD",
        "outputId": "b7164457-9dbb-4eb9-d8f5-f55f800ff371"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1fb97a11e0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x3000 with 1 Axes>"
            ],
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}