table-ocr

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 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