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                TABLE DETECTION IN IMAGES AND OCR TO CSV

                               Eric Ihli
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Table of Contents
─────────────────

1. Overview
2. Requirements
3. Demo
4. Modules





1 Overview
══════════

  This python package contains modules to help with finding and
  extracting tabular data from a PDF or image into a CSV format.

  Given an image that contains a table…

  <file:resources/examples/example-page.png>

  Extract the the text into a CSV format…

  ┌────
  │ PRIZE,ODDS 1 IN:,# OF WINNERS*
  │ $3,9.09,"282,447"
  │ $5,16.66,"154,097"
  │ $7,40.01,"64,169"
  │ $10,26.67,"96,283"
  │ $20,100.00,"25,677"
  │ $30,290.83,"8,829"
  │ $50,239.66,"10,714"
  │ $100,919.66,"2,792"
  │ $500,"6,652.07",386
  │ "$40,000","855,899.99",3
  │ 1,i223,
  │ Toa,,
  │ ,,
  │ ,,"* Based upon 2,567,700"
  └────


2 Requirements
══════════════

  Along with the python requirements that are listed in setup.py and
  that are automatically installed when installing this package through
  pip, there are a few external requirements for some of the modules.

  I haven’t looked into the minimum required versions of these
  dependencies, but I’ll list the versions that I’m using.

  • `pdfimages' 20.09.0 of [Poppler]
  • `tesseract' 5.0.0 of [Tesseract]
  • `mogrify' 7.0.10 of [ImageMagick]


[Poppler] <https://poppler.freedesktop.org/>

[Tesseract] <https://github.com/tesseract-ocr/tesseract>

[ImageMagick] <https://imagemagick.org/index.php>


3 Demo
══════

  There is a demo module that will download an image given a URL and try
  to extract tables from the image and process the cells into a CSV. You
  can try it out with one of the images included in this repo.

  1. `pip3 install table_ocr'
  2. `python3 -m table_ocr.demo
     https://raw.githubusercontent.com/eihli/image-table-ocr/master/resources/test_data/simple.png'

  That will run against the following image:

  <file:resources/test_data/simple.png>

  The following should be printed to your terminal after running the
  above commands.

  ┌────
  │ Running `extract_tables.main([/tmp/demo_p9on6m8o/simple.png]).`
  │ Extracted the following tables from the image:
  │ [('/tmp/demo_p9on6m8o/simple.png', ['/tmp/demo_p9on6m8o/simple/table-000.png'])]
  │ Processing tables for /tmp/demo_p9on6m8o/simple.png.
  │ Processing table /tmp/demo_p9on6m8o/simple/table-000.png.
  │ Extracted 18 cells from /tmp/demo_p9on6m8o/simple/table-000.png
  │ Cells:
  │ /tmp/demo_p9on6m8o/simple/cells/000-000.png: Cell
  │ /tmp/demo_p9on6m8o/simple/cells/000-001.png: Format
  │ /tmp/demo_p9on6m8o/simple/cells/000-002.png: Formula
  │ ...
  │ 
  │ Here is the entire CSV output:
  │ 
  │ Cell,Format,Formula
  │ B4,Percentage,None
  │ C4,General,None
  │ D4,Accounting,None
  │ E4,Currency,"=PMT(B4/12,C4,D4)"
  │ F4,Currency,=E4*C4
  └────


4 Modules
═════════

  The package is split into modules with narrow focuses.

  • `pdf_to_images' uses Poppler and ImageMagick to extract images from
    a PDF.
  • `extract_tables' finds and extracts table-looking things from an
    image.
  • `extract_cells' extracts and orders cells from a table.
  • `ocr_image' uses Tesseract to OCR the text from an image of a cell.
  • `ocr_to_csv' converts into a CSV the directory structure that
    `ocr_image' outputs.

  The outputs of a previous module can be used by a subsequent module so
  that they can be chained together to create the entire workflow, as
  demonstrated by the following shell script.

  ┌────
  │ #!/bin/sh
  │ 
  │ PDF=$1
  │ 
  │ python -m table_ocr.pdf_to_images $PDF | grep .png > /tmp/pdf-images.txt
  │ cat /tmp/pdf-images.txt | xargs -I{} python -m table_ocr.extract_tables {}  | grep table > /tmp/extracted-tables.txt
  │ cat /tmp/extracted-tables.txt | xargs -I{} python -m table_ocr.extract_cells {} | grep cells > /tmp/extracted-cells.txt
  │ cat /tmp/extracted-cells.txt | xargs -I{} python -m table_ocr.ocr_image {}
  │ 
  │ for image in $(cat /tmp/extracted-tables.txt); do
  │     dir=$(dirname $image)
  │     python -m table_ocr.ocr_to_csv $(find $dir/cells -name "*.txt")
  │ done
  └────


  The package was written in a [literate programming] style. The source
  code at
  <https://eihli.github.io/image-table-ocr/pdf_table_extraction_and_ocr.html>
  is meant to act as the documentation and reference material.


[literate programming]
<https://en.wikipedia.org/wiki/Literate_programming>