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TABLE DETECTION IN IMAGES AND OCR TO CSV
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Eric Ihli
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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Table of Contents
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─────────────────
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1. Overview
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2. Requirements
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3. Demo
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4. Modules
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1 Overview
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══════════
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This python package contains modules to help with finding and
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extracting tabular data from a PDF or image into a CSV format.
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Given an image that contains a table…
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<file:resources/examples/example-page.png>
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Extract the the text into a CSV format…
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┌────
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│ PRIZE,ODDS 1 IN:,# OF WINNERS*
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│ $3,9.09,"282,447"
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│ $5,16.66,"154,097"
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│ $7,40.01,"64,169"
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│ $10,26.67,"96,283"
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│ $20,100.00,"25,677"
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│ $30,290.83,"8,829"
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│ $50,239.66,"10,714"
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│ $100,919.66,"2,792"
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│ $500,"6,652.07",386
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│ "$40,000","855,899.99",3
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│ 1,i223,
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│ Toa,,
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│ ,,
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│ ,,"* Based upon 2,567,700"
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└────
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2 Requirements
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══════════════
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Along with the python requirements that are listed in setup.py and
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that are automatically installed when installing this package through
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pip, there are a few external requirements for some of the modules.
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I haven’t looked into the minimum required versions of these
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dependencies, but I’ll list the versions that I’m using.
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• `pdfimages' 20.09.0 of [Poppler]
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• `tesseract' 5.0.0 of [Tesseract]
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• `mogrify' 7.0.10 of [ImageMagick]
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[Poppler] <https://poppler.freedesktop.org/>
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[Tesseract] <https://github.com/tesseract-ocr/tesseract>
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[ImageMagick] <https://imagemagick.org/index.php>
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3 Demo
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══════
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There is a demo module that will download an image given a URL and try
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to extract tables from the image and process the cells into a CSV. You
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can try it out with one of the images included in this repo.
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1. `pip3 install table_ocr'
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2. `python3 -m table_ocr.demo
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https://raw.githubusercontent.com/eihli/image-table-ocr/master/resources/test_data/simple.png'
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That will run against the following image:
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<file:resources/test_data/simple.png>
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The following should be printed to your terminal after running the
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above commands.
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┌────
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│ Running `extract_tables.main([/tmp/demo_p9on6m8o/simple.png]).`
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│ Extracted the following tables from the image:
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│ [('/tmp/demo_p9on6m8o/simple.png', ['/tmp/demo_p9on6m8o/simple/table-000.png'])]
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│ Processing tables for /tmp/demo_p9on6m8o/simple.png.
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│ Processing table /tmp/demo_p9on6m8o/simple/table-000.png.
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│ Extracted 18 cells from /tmp/demo_p9on6m8o/simple/table-000.png
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│ Cells:
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│ /tmp/demo_p9on6m8o/simple/cells/000-000.png: Cell
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│ /tmp/demo_p9on6m8o/simple/cells/000-001.png: Format
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│ /tmp/demo_p9on6m8o/simple/cells/000-002.png: Formula
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│ ...
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│
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│ Here is the entire CSV output:
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│
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│ Cell,Format,Formula
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│ B4,Percentage,None
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│ C4,General,None
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│ D4,Accounting,None
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│ E4,Currency,"=PMT(B4/12,C4,D4)"
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│ F4,Currency,=E4*C4
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└────
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4 Modules
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═════════
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The package is split into modules with narrow focuses.
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• `pdf_to_images' uses Poppler and ImageMagick to extract images from
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a PDF.
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• `extract_tables' finds and extracts table-looking things from an
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image.
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• `extract_cells' extracts and orders cells from a table.
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• `ocr_image' uses Tesseract to OCR the text from an image of a cell.
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• `ocr_to_csv' converts into a CSV the directory structure that
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`ocr_image' outputs.
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The outputs of a previous module can be used by a subsequent module so
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that they can be chained together to create the entire workflow, as
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demonstrated by the following shell script.
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┌────
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│ #!/bin/sh
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│
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│ PDF=$1
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│
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│ python -m table_ocr.pdf_to_images $PDF | grep .png > /tmp/pdf-images.txt
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│ cat /tmp/pdf-images.txt | xargs -I{} python -m table_ocr.extract_tables {} | grep table > /tmp/extracted-tables.txt
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│ cat /tmp/extracted-tables.txt | xargs -I{} python -m table_ocr.extract_cells {} | grep cells > /tmp/extracted-cells.txt
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│ cat /tmp/extracted-cells.txt | xargs -I{} python -m table_ocr.ocr_image {}
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│
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│ for image in $(cat /tmp/extracted-tables.txt); do
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│ dir=$(dirname $image)
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│ python -m table_ocr.ocr_to_csv $(find $dir/cells -name "*.txt")
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│ done
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└────
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The package was written in a [literate programming] style. The source
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code at
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<https://eihli.github.io/image-table-ocr/pdf_table_extraction_and_ocr.html>
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is meant to act as the documentation and reference material.
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[literate programming]
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<https://en.wikipedia.org/wiki/Literate_programming>
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