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125 lines
4.1 KiB
Markdown
125 lines
4.1 KiB
Markdown
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# Table of Contents
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1. [Overview](#org9905778)
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2. [Requirements](#org08346b2)
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3. [Demo](#orgb3d60f1)
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4. [Modules](#org67b1fc2)
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<a id="org9905778"></a>
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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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Extract the the text into a CSV format…
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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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<a id="org08346b2"></a>
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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 [Poppler](https://poppler.freedesktop.org/)
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- `tesseract` 5.0.0 of [Tesseract](https://github.com/tesseract-ocr/tesseract)
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- `mogrify` 7.0.10 of [ImageMagick](https://imagemagick.org/index.php)
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<a id="orgb3d60f1"></a>
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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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The following should be printed to your terminal after running the above commands.
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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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<a id="org67b1fc2"></a>
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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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#!/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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The package was written in a [literate programming](https://en.wikipedia.org/wiki/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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