Untitled82.ipynb

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  "metadata": {
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      "provenance": [],
      "authorship_tag": "ABX9TyN/rqgAQJi0ZhD3ikn1T28w",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sonigovind/Assignment1/blob/main/Untitled82.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Extracted images fromt the scanned pdf**"
      ],
      "metadata": {
        "id": "NsZJruSU3CRr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xGbclT6ukhbs",
        "outputId": "3681d859-bd74-4e28-b756-3a099c892a7b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: PyMuPDF in /usr/local/lib/python3.7/dist-packages (1.20.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install PyMuPDF"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install Pillow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sB8VMcgbkmxh",
        "outputId": "017a600b-5625-4ca8-b870-234da8ae9d6a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (7.1.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import fitz # PyMuPDF\n",
        "import io\n",
        "from PIL import Image"
      ],
      "metadata": {
        "id": "pJwNAbydkuog"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# file path you want to extract images from\n",
        "file = \"/content/1 Work_Experience_compressed.pdf\"\n",
        "# open the file\n",
        "pdf_file = fitz.open(file)"
      ],
      "metadata": {
        "id": "t6nFaQZYkxfT"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# iterate over PDF pages\n",
        "for page_index in range(len(pdf_file)):\n",
        "    # get the page itself\n",
        "    page = pdf_file[page_index]\n",
        "    image_list = page.get_images()\n",
        "    # printing number of images found in this page\n",
        "    if image_list:\n",
        "        print(f\"[+] Found a total of {len(image_list)} images in page {page_index}\")\n",
        "    else:\n",
        "        print(\"[!] No images found on page\", page_index)\n",
        "    for image_index, img in enumerate(page.get_images(), start=1):\n",
        "        # get the XREF of the image\n",
        "        xref = img[0]\n",
        "        # extract the image bytes\n",
        "        base_image = pdf_file.extract_image(xref)\n",
        "        image_bytes = base_image[\"image\"]\n",
        "        # get the image extension\n",
        "        image_ext = base_image[\"ext\"]\n",
        "        # load it to PIL\n",
        "        image = Image.open(io.BytesIO(image_bytes))\n",
        "        # save it to local disk\n",
        "        image.save(open(f\"image{page_index+1}_{image_index}.{image_ext}\", \"wb\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hP9UD8ShkxjB",
        "outputId": "25fa3ac0-0c84-40b7-8673-d03a48c426a4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[+] Found a total of 1 images in page 0\n",
            "[+] Found a total of 1 images in page 1\n",
            "[+] Found a total of 1 images in page 2\n",
            "[+] Found a total of 1 images in page 3\n",
            "[+] Found a total of 1 images in page 4\n",
            "[+] Found a total of 1 images in page 5\n",
            "[+] Found a total of 1 images in page 6\n",
            "[+] Found a total of 1 images in page 7\n",
            "[+] Found a total of 1 images in page 8\n",
            "[+] Found a total of 1 images in page 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Extracting Multi-Column tabular data from IMAGE**"
      ],
      "metadata": {
        "id": "5Z_wP8gk4AAo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!add-apt-repository -y ppa:alex-p/tesseract-ocr-devel\n",
        "!apt-get update\n",
        "!apt-get install tesseract-ocr\n",
        "!pip install pytesseract"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p_q7_0NKszXg",
        "outputId": "de772e0d-7d41-4b0d-c814-740794060783"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building dependency tree       \n",
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            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following additional packages will be installed:\n",
            "  libtesseract5 tesseract-ocr-eng tesseract-ocr-osd\n",
            "The following NEW packages will be installed:\n",
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            "  tesseract-ocr tesseract-ocr-eng tesseract-ocr-osd\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import **Packages**"
      ],
      "metadata": {
        "id": "GZv5G2pl4PYv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import the necessary packages\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "from matplotlib import pyplot as plt\n",
        "from pytesseract import Output\n",
        "from tabulate import tabulate\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import pytesseract\n",
        "import argparse\n",
        "import imutils\n",
        "import cv2"
      ],
      "metadata": {
        "id": "PwqLbngltH_Q"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Function to display images in Jupyter Notebooks and Google Colab"
      ],
      "metadata": {
        "id": "2PFddXtM4Wog"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plt_imshow(title, image):\n",
        "\t# convert the image frame BGR to RGB color space and display it\n",
        "\timage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "\tplt.imshow(image)\n",
        "\tplt.title(title)\n",
        "\tplt.grid(False)\n",
        "\tplt.show()"
      ],
      "metadata": {
        "id": "SZUOB-AE1b6f"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Implementing Multi-Column Table OCR"
      ],
      "metadata": {
        "id": "iy00TLUn4cNv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "args = {\n",
        "\t\"image\": \"/content/image1_1.png\",\n",
        "\t\"output\": \"results.csv\",\n",
        "\t\"min_conf\": 0,\n",
        "\t\"dist_thresh\": 25.0,\n",
        "\t\"min_size\": 2,\n",
        "}"
      ],
      "metadata": {
        "id": "DTsQxz9y1m3O"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# set a seed for our random number generator\n",
        "np.random.seed(42)\n",
        "\n",
        "# load the input image and convert it to grayscale\n",
        "image = cv2.imread(args[\"image\"])\n",
        "gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)"
      ],
      "metadata": {
        "id": "Shg48xBd1uAe"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (51, 11))\n",
        "gray = cv2.GaussianBlur(gray, (3, 3), 0)\n",
        "blackhat = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, kernel)\n",
        "\n",
        "# compute the Scharr gradient of the blackhat image and scale the\n",
        "# result into the range [0, 255]\n",
        "grad = cv2.Sobel(blackhat, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)\n",
        "grad = np.absolute(grad)\n",
        "(minVal, maxVal) = (np.min(grad), np.max(grad))\n",
        "grad = (grad - minVal) / (maxVal - minVal)\n",
        "grad = (grad * 255).astype(\"uint8\")\n",
        "\n",
        "# apply a closing operation using the rectangular kernel to close\n",
        "# gaps in between characters, apply Otsu's thresholding method, and\n",
        "# finally a dilation operation to enlarge foreground regions\n",
        "grad = cv2.morphologyEx(grad, cv2.MORPH_CLOSE, kernel)\n",
        "thresh = cv2.threshold(grad, 0, 255,\n",
        "\tcv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]\n",
        "thresh = cv2.dilate(thresh, None, iterations=3)\n",
        "plt_imshow(\"Thresh\", thresh)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "f_33WVQa1xXe",
        "outputId": "8bb6f78e-e228-4567-dce7-cd6a5e2ab5f8"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,\n",
        "\tcv2.CHAIN_APPROX_SIMPLE)\n",
        "cnts = imutils.grab_contours(cnts)\n",
        "tableCnt = max(cnts, key=cv2.contourArea)\n",
        "\n",
        "# compute the bounding box coordinates of the stats table and extract\n",
        "# the table from the input image\n",
        "(x, y, w, h) = cv2.boundingRect(tableCnt)\n",
        "table = image[y:y + h, x:x + w]\n",
        "\n",
        "# show the original input image and extracted table to our screen\n",
        "plt_imshow(\"Input\", image)\n",
        "plt_imshow(\"Table\", table)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "3c-09LMm11Iy",
        "outputId": "21230a30-059d-4aac-ef71-277876b60d48"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# set the PSM mode to detect sparse text, and then localize text in\n",
        "# the table\n",
        "options = \"--psm 6\"\n",
        "results = pytesseract.image_to_data(\n",
        "\tcv2.cvtColor(table, cv2.COLOR_BGR2RGB),\n",
        "\tconfig=options,\n",
        "\toutput_type=Output.DICT)\n",
        "\n",
        "# initialize a list to store the (x, y)-coordinates of the detected\n",
        "# text along with the OCR'd text itself\n",
        "coords = []\n",
        "ocrText = []\n",
        "\n",
        "# loop over each of the individual text localizations\n",
        "for i in range(0, len(results[\"text\"])):\n",
        "\t# extract the bounding box coordinates of the text region from\n",
        "\t# the current result\n",
        "\tx = results[\"left\"][i]\n",
        "\ty = results[\"top\"][i]\n",
        "\tw = results[\"width\"][i]\n",
        "\th = results[\"height\"][i]\n",
        "\n",
        "\t# extract the OCR text itself along with the confidence of the\n",
        "\t# text localization\n",
        "\ttext = results[\"text\"][i]\n",
        "\tconf = int(float(results[\"conf\"][i]))\n",
        "\n",
        "\t# filter out weak confidence text localizations\n",
        "\tif conf > args[\"min_conf\"]:\n",
        "\t\t# update our text bounding box coordinates and OCR'd text,\n",
        "\t\t# respectively\n",
        "\t\tcoords.append((x, y, w, h))\n",
        "\t\tocrText.append(text)"
      ],
      "metadata": {
        "id": "lFY1dQAF15Oy"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# extract all x-coordinates from the text bounding boxes, setting the\n",
        "# y-coordinate value to zero\n",
        "xCoords = [(c[0], 0) for c in coords]\n",
        "\n",
        "# apply hierarchical agglomerative clustering to the coordinates\n",
        "clustering = AgglomerativeClustering(\n",
        "\tn_clusters=None,\n",
        "\taffinity=\"manhattan\",\n",
        "\tlinkage=\"complete\",\n",
        "\tdistance_threshold=args[\"dist_thresh\"])\n",
        "clustering.fit(xCoords)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FcpALwhE1--S",
        "outputId": "c697a52d-9760-41a7-8846-5f4e9d9e565a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AgglomerativeClustering(affinity='manhattan', distance_threshold=25.0,\n",
              "                        linkage='complete', n_clusters=None)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# initialize our list of sorted clusters\n",
        "sortedClusters = []\n",
        "\n",
        "# loop over all clusters\n",
        "for l in np.unique(clustering.labels_):\n",
        "\t# extract the indexes for the coordinates belonging to the\n",
        "\t# current cluster\n",
        "\tidxs = np.where(clustering.labels_ == l)[0]\n",
        "\n",
        "\t# verify that the cluster is sufficiently large\n",
        "\tif len(idxs) > args[\"min_size\"]:\n",
        "\t\t# compute the average x-coordinate value of the cluster and\n",
        "\t\t# update our clusters list with the current label and the\n",
        "\t\t# average x-coordinate\n",
        "\t\tavg = np.average([coords[i][0] for i in idxs])\n",
        "\t\tsortedClusters.append((l, avg))\n",
        "\n",
        "# sort the clusters by their average x-coordinate and initialize our\n",
        "# data frame\n",
        "sortedClusters.sort(key=lambda x: x[1])\n",
        "df = pd.DataFrame()"
      ],
      "metadata": {
        "id": "LmEJavGa2GPw"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loop over the clusters again, this time in sorted order\n",
        "for (l, _) in sortedClusters:\n",
        "\t# extract the indexes for the coordinates belonging to the\n",
        "\t# current cluster\n",
        "\tidxs = np.where(clustering.labels_ == l)[0]\n",
        "\n",
        "\t# extract the y-coordinates from the elements in the current\n",
        "\t# cluster, then sort them from top-to-bottom\n",
        "\tyCoords = [coords[i][1] for i in idxs]\n",
        "\tsortedIdxs = idxs[np.argsort(yCoords)]\n",
        "\n",
        "\t# generate a random color for the cluster\n",
        "\tcolor = np.random.randint(0, 255, size=(3,), dtype=\"int\")\n",
        "\tcolor = [int(c) for c in color]\n",
        "\n",
        "\t# loop over the sorted indexes\n",
        "\tfor i in sortedIdxs:\n",
        "\t\t# extract the text bounding box coordinates and draw the\n",
        "\t\t# bounding box surrounding the current element\n",
        "\t\t(x, y, w, h) = coords[i]\n",
        "\t\tcv2.rectangle(table, (x, y), (x + w, y + h), color, 2)\n",
        "\n",
        "\t# extract the OCR'd text for the current column, then construct\n",
        "\t# a data frame for the data where the first entry in our column\n",
        "\t# serves as the header\n",
        "\tcols = [ocrText[i].strip() for i in sortedIdxs]\n",
        "\tcurrentDF = pd.DataFrame({cols[0]: cols[1:]})\n",
        "\n",
        "\t# concatenate *original* data frame with the *current* data\n",
        "\t# frame (we do this to handle columns that may have a varying\n",
        "\t# number of rows)\n",
        "\tdf = pd.concat([df, currentDF], axis=1)"
      ],
      "metadata": {
        "id": "FKju3Bed2J0_"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multi-Column OCR results"
      ],
      "metadata": {
        "id": "SsK8FAEM4j1f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# replace NaN values with an empty string and then show a nicely\n",
        "# formatted version of our multi-column OCR'd text\n",
        "df.fillna(\"\", inplace=True)\n",
        "print(tabulate(df, headers=\"keys\", tablefmt=\"psql\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y3tZ8LrO2NLg",
        "outputId": "86dc017c-ad8b-449a-d93b-4f424ffd0d40"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+-------------+-------------+-----+---------------+------+-----+-----------------+-----------+-----+-----------------------------+-----+-----------------+------+------------------+-----+-----------------+-------+\n",
            "|    | FINANCIAL   | WORK        | |   | PERIOD        | OF   | |   | pescriprr       | WORK      | |   | CONTRACT                    | |   | EXECUTED        | IN   | |                | |   | VALUE           | FIN   |\n",
            "|----+-------------+-------------+-----+---------------+------+-----+-----------------+-----------+-----+-----------------------------+-----+-----------------+------+------------------+-----+-----------------+-------|\n",
            "|  0 | |           | ORDER       | |   | ORDER         | ORE  | |   | LOCATION        | ONS       | |   | VALUE                       | _   | VALUE           | YEAR | AMOUNTIN         | i   | (INCLUDING      | YEAR  |\n",
            "|  1 | |           | NUMBER      | |   | 01.09.2019    | on   | |   | Mining,(inclu   | drilling, | |   | 1,01,01,05,000              | |   | EXCLUDING       | RS(  | 5,58,388.26      | |   | 36,60,545.26    | Gs    |\n",
            "|  2 |             | 2020/200042 | |   | 31.08.2020    |      | |   | des             | and       | |   | 77,85,000                   | _|  | 31,02,157       | GST. | 5,18,741.64      | |   | 34,00,639.64    |       |\n",
            "|  3 |             | 2020/200063 | |   | 01.12.2019    |      | |   | excavation,     | drilling, | |   | 74.73,30,000__|_3.49,17,924 |     | 28,81,898       |      | 62,85,226.32___| | |   | 4,12,03,150.32  |       |\n",
            "|  4 |             | 2020/200064 |     | -             |      | |   | dumping,        | and       |     | 62,00,92,590                |     | 13.62.15,187__| |      | 2,45,18,733.66   | |   | 16,07,33,920.66 |       |\n",
            "|  5 |             | 2020/200092 |     | 30.11.2020    |      | |   | spreading,      | drilling, |     | 16.30,30,000                |     | 84,46,135       |      | 15,20,304.30___| |     | 99,66,439.30    |       |\n",
            "|  6 |             | 2019/200076 |     | 01.12.2019    |      | |   | dozing),Loadi   | and       |     |                             |     |                 |      |                  |     |                 |       |\n",
            "|  7 |             |             |     | :             |      | |   | ng              | drilling. |     |                             |     |                 |      |                  |     |                 |       |\n",
            "|  8 |             |             |     | 30.11.2020    |      | |   | ‘Transportation | and       |     |                             |     |                 |      |                  |     |                 |       |\n",
            "|  9 |             |             |     | (Gworlcorder| |      | |   | Chhotedongar    | drilling, |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 10 |             |             |     | short         |      | |   | des             | and       |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 11 |             |             |     | closed        |      |     | excavation,     |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 12 |             |             |     | 04.01.2020    |      |     | dumping,        |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 13 |             |             |     | )             |      |     | spreading,      |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 14 |             |             |     | 05.01.2020    |      |     | dozing),Loadi   |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 15 |             |             |     | -             |      |     | ng              |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 16 |             |             |     | 30.11.2020    |      |     | Transportation  |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 17 |             |             |     | 01.12.2018    |      |     | -Laindongri     |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 18 |             |             |     | -             |      |     | Mining,(inclu   |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 19 |             |             |     | 30.11.2019    |      |     | des             |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 20 |             |             |     |               |      |     | excavation,     |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 21 |             |             |     |               |      |     | dumping,        |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 22 |             |             |     |               |      |     | ‘spreading,     |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 23 |             |             |     |               |      |     | dozing).Loadi   |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 24 |             |             |     |               |      |     | ng              |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 25 |             |             |     |               |      |     | Transportation  |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 26 |             |             |     |               |      |     | -Metabodeli__|  |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 27 |             |             |     |               |      |     | Mining,(inciu   |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 28 |             |             |     |               |      |     | des             |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 29 |             |             |     |               |      |     | excavation,     |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 30 |             |             |     |               |      |     | dumping,        |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 31 |             |             |     |               |      |     | spreading,      |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 32 |             |             |     |               |      |     | dozing),Loadi   |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 33 |             |             |     |               |      |     | ng              |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 34 |             |             |     |               |      |     | Transportation  |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 35 |             |             |     |               |      |     | -Metabodeli__|  |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 36 |             |             |     |               |      |     | Mining,(inclu   |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 37 |             |             |     |               |      |     | des             |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 38 |             |             |     |               |      |     | excavation,     |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 39 |             |             |     |               |      |     | dumping.        |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 40 |             |             |     |               |      |     | spreading.      |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 41 |             |             |     |               |      |     | dozing),Loadi   |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 42 |             |             |     |               |      |     | ng              |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 43 |             |             |     |               |      |     | Transportation  |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "| 44 |             |             |     |               |      |     | -Laindongri     |           |     |                             |     |                 |      |                  |     |                 |       |\n",
            "+----+-------------+-------------+-----+---------------+------+-----+-----------------+-----------+-----+-----------------------------+-----+-----------------+------+------------------+-----+-----------------+-------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# write our table to disk as a CSV file\n",
        "print(\"[INFO] saving CSV file to disk...\")\n",
        "df.to_csv(args[\"output\"], index=False)\n",
        "\n",
        "# show the output image after performing multi-column OCR\n",
        "plt_imshow(\"Output\", image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "QCDOa4VF2Qyg",
        "outputId": "d732cc11-b301-4452-c9ee-0a53f1d74be8"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INFO] saving CSV file to disk...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LPmiBPTJ2WYA"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}