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115 lines
4.0 KiB
Org Mode
115 lines
4.0 KiB
Org Mode
#+TITLE: Table detection in images and OCR to CSV
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* Overview
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This python package contains modules to help with finding and extracting tabular
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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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#+ATTR_HTML: :width 25%
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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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#+BEGIN_EXAMPLE
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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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#+END_EXAMPLE
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* Requirements
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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.
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I haven't looked into the minimum required versions of these dependencies, but I'll list the versions that I'm using.
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- ~pdfimages~ 20.09.0 of [[https://poppler.freedesktop.org/][Poppler]]
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- ~tesseract~ 5.0.0 of [[https://github.com/tesseract-ocr/tesseract][Tesseract]]
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- ~mogrify~ 7.0.10 of [[https://imagemagick.org/index.php][ImageMagick]]
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* Demo
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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.
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1. ~pip3 install table_ocr~
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2. ~python3 -m table_ocr.demo 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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#+ATTR_HTML: :width 40%
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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 above commands.
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#+BEGIN_EXAMPLE
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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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Here is the entire CSV output:
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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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#+END_EXAMPLE
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* Modules
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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 a PDF.
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- ~extract_tables~ finds and extracts table-looking things from an 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 ~ocr_image~ outputs.
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The outputs of a previous module can be used by a subsequent module so that they
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can be chained together to create the entire workflow, as demonstrated by the
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following shell script.
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#+NAME: ocr_tables
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#+BEGIN_SRC shell :results none :tangle ocr_tables :tangle-mode (identity #o755)
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#!/bin/sh
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PDF=$1
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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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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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#+END_SRC
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The package was written in a [[https://en.wikipedia.org/wiki/Literate_programming][literate programming]] style. The source code at
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[[https://eihli.github.io/image-table-ocr/pdf_table_extraction_and_ocr.html]] is
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meant to act as the documentation and reference material.
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