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<div id="content">
<h1 class="title">PDF Parsing</h1>
<div id="table-of-contents">
<h2>Table of Contents</h2>
<div id="text-table-of-contents">
<ul>
<li><a href="#org3fab902">1. Overview</a>
<ul>
<li><a href="#orgaf477b8">1.1. Requirements</a>
<ul>
<li><a href="#org43dd3dc">1.1.1. Python packages</a></li>
<li><a href="#org8927075">1.1.2. External</a></li>
</ul>
</li>
<li><a href="#org14c36da">1.2. Contributing</a></li>
<li><a href="#org8aef2ca">1.3. Example usage</a></li>
<li><a href="#org7e5cd11">1.4. Possible improvements</a></li>
</ul>
</li>
<li><a href="#org51af43b">2. Preparing data</a>
<ul>
<li><a href="#orga4dde96">2.1. Converting PDFs to images</a></li>
<li><a href="#org6c75ffa">2.2. Detecting image orientation and applying rotation.</a></li>
</ul>
</li>
<li><a href="#orgc195fec">3. Detecting tables</a>
<ul>
<li><a href="#org9dd75a6">3.1. Improving accuracy</a></li>
</ul>
</li>
<li><a href="#org904debc">4. OCR tables</a>
<ul>
<li><a href="#orgb03a965">4.1. Training Tesseract</a>
<ul>
<li><a href="#org5adeb27">4.1.1. Training tips</a></li>
</ul>
</li>
<li><a href="#org152ead5">4.2. Blur</a></li>
<li><a href="#org858fb89">4.3. Threshold</a></li>
<li><a href="#orgcf17042">4.4. Finding the vertical and horizontal lines of the table</a></li>
<li><a href="#org94f71b3">4.5. Finding the contours</a></li>
<li><a href="#orgc64b6ef">4.6. Sorting the bounding rectangles</a></li>
<li><a href="#orgd4dc4cc">4.7. Cropping each cell to the text</a></li>
<li><a href="#org22a3e7b">4.8. OCR each cell</a></li>
</ul>
</li>
<li><a href="#org9d0b21d">5. Files</a>
<ul>
<li><a href="#orgd57e56a">5.1. setup.py</a></li>
<li><a href="#org4b36161">5.2. table_ocr</a>
<ul>
<li><a href="#orgbdd2fc0">5.2.1. table_ocr/__init__.py</a></li>
<li><a href="#org09e5a07">5.2.2. table_ocr/util.py</a></li>
<li><a href="#org5a371bd">5.2.3. table_ocr/pdf_to_images/</a>
<ul>
<li><a href="#orgd777fae">5.2.3.1. table_ocr/pdf_to_images/__init__.py</a></li>
<li><a href="#org0064754">5.2.3.2. table_ocr/pdf_to_images/__main__.py</a></li>
</ul>
</li>
<li><a href="#org03e58e9">5.2.4. table_ocr/extract_tables/</a>
<ul>
<li><a href="#orgfedc867">5.2.4.1. table_ocr/extract_tables/__init__.py</a></li>
<li><a href="#org82b2c3a">5.2.4.2. table_ocr/extract_tables/__main__.py</a></li>
</ul>
</li>
<li><a href="#org7ec79e9">5.2.5. table_ocr/extract_cells/</a>
<ul>
<li><a href="#org6d6ddc7">5.2.5.1. table_ocr/extract_cells/__init__.py</a></li>
<li><a href="#orgd698866">5.2.5.2. table_ocr/extract_cells/__main__.py</a></li>
</ul>
</li>
<li><a href="#org5ff2e40">5.2.6. table_ocr/ocr_image/</a>
<ul>
<li><a href="#org1bc0eb3">5.2.6.1. table_ocr/ocr_image/__init__.py</a></li>
<li><a href="#org11f1d0c">5.2.6.2. table_ocr/ocr_image/__main__.py</a></li>
</ul>
</li>
<li><a href="#org7612c04">5.2.7. table_ocr/ocr_to_csv/</a>
<ul>
<li><a href="#orgb76e923">5.2.7.1. table_ocr/ocr_to_csv/__init__.py</a></li>
<li><a href="#orgb9ce258">5.2.7.2. table_ocr/ocr_to_csv/__main__.py</a></li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
<li><a href="#org446b9ad">6. Utils</a>
<ul>
<li><a href="#orgac512bd">6.1. Logging</a></li>
</ul>
</li>
</ul>
</div>
</div>
<div id="outline-container-org3fab902" class="outline-2">
<h2 id="org3fab902"><span class="section-number-2">1</span> Overview</h2>
<div class="outline-text-2" id="text-1">
<p>
This Python package provides utilities for extracting tabular data from PDF
files and images of tables.
</p>
<p>
Given an image that contains a table…
</p>
<div class="figure">
<p><img src="resources/examples/example-page.png" alt="example-page.png" width="25%" />
</p>
</div>
<p>
Extract the the text into a CSV format…
</p>
<pre class="example">
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"
</pre>
<p>
The package is split into modules with narrow focuses.
</p>
<ul class="org-ul">
<li><code>pdf_to_images</code> uses Poppler and ImageMagick to extract images from a PDF.</li>
<li><code>extract_tables</code> finds and extracts table-looking things from an image.</li>
<li><code>extract_cells</code> extracts and orders cells from a table.</li>
<li><code>ocr_image</code> uses Tesseract to OCR the text from an image of a cell.</li>
<li><code>ocr_to_csv</code> converts into a CSV the directory structure that <code>ocr_image</code> outputs.</li>
</ul>
</div>
<div id="outline-container-orgaf477b8" class="outline-3">
<h3 id="orgaf477b8"><span class="section-number-3">1.1</span> Requirements</h3>
<div class="outline-text-3" id="text-1-1">
</div>
<div id="outline-container-org43dd3dc" class="outline-4">
<h4 id="org43dd3dc"><span class="section-number-4">1.1.1</span> Python packages</h4>
<div class="outline-text-4" id="text-1-1-1">
<ul class="org-ul">
<li>numpy</li>
<li>opencv-python</li>
<li>pytesseract</li>
</ul>
</div>
</div>
<div id="outline-container-org8927075" class="outline-4">
<h4 id="org8927075"><span class="section-number-4">1.1.2</span> External</h4>
<div class="outline-text-4" id="text-1-1-2">
<ul class="org-ul">
<li><code>pdfimages</code> from Poppler</li>
<li>Tesseract</li>
<li><code>mogfrify</code> ImageMagick</li>
</ul>
</div>
</div>
</div>
<div id="outline-container-org14c36da" class="outline-3">
<h3 id="org14c36da"><span class="section-number-3">1.2</span> Contributing</h3>
<div class="outline-text-3" id="text-1-2">
<p>
This package was created in a <a href="https://en.wikipedia.org/wiki/Literate_programming">literate programming</a> style with the help of <a href="https://orgmode.org/worg/org-contrib/babel/intro.html">Babel</a>.
</p>
<p>
The unfortunate downside is the obscurity of the tooling. It creates a bit of a
barrier for contributors who aren’t already familiar with Emacs and Babel.
</p>
</div>
</div>
<div id="outline-container-org8aef2ca" class="outline-3">
<h3 id="org8aef2ca"><span class="section-number-3">1.3</span> Example usage</h3>
<div class="outline-text-3" id="text-1-3">
<p>
Here is an example of a shell script that uses each module to turn a pdf with a
table into CSV output.
</p>
<p>
Depending on your needs, you may not need all of these steps. If you already
have an image of a table, you can jum straight to extracting the cells.
</p>
<p>
Each piece is its own python module, so you can also simply import the pieces
you need into your own python projects and use them as needed.
</p>
<div class="org-src-container">
<pre class="src src-shell" id="org5f5c842"><span style="color: #5B6268;">#</span><span style="color: #5B6268;">!/bin/</span><span style="color: #51afef;">sh</span>
<span style="color: #dcaeea;">PDF</span>=$<span style="color: #da8548; font-weight: bold;">1</span>
python -m table_ocr.pdf_to_images $<span style="color: #dcaeea;">PDF</span> | <span style="color: #ECBE7B;">grep</span> .png > /tmp/pdf-images.txt
<span style="color: #ECBE7B;">cat</span> /tmp/pdf-images.txt | xargs -I<span style="color: #51afef;">{}</span> python -m table_ocr.extract_tables <span style="color: #51afef;">{}</span> | <span style="color: #ECBE7B;">grep</span> table > /tmp/extracted-tables.txt
<span style="color: #ECBE7B;">cat</span> /tmp/extracted-tables.txt | xargs -I<span style="color: #51afef;">{}</span> python -m table_ocr.extract_cells <span style="color: #51afef;">{}</span> | <span style="color: #ECBE7B;">grep</span> cells > /tmp/extracted-cells.txt
<span style="color: #ECBE7B;">cat</span> /tmp/extracted-cells.txt | xargs -I<span style="color: #51afef;">{}</span> python -m table_ocr.ocr_image <span style="color: #51afef;">{}</span>
<span style="color: #51afef;">for</span> image<span style="color: #51afef;"> in</span> $<span style="color: #51afef;">(</span><span style="color: #ECBE7B;">cat</span> /tmp/extracted-tables.txt<span style="color: #51afef;">)</span>; <span style="color: #51afef;">do</span>
<span style="color: #dcaeea;">dir</span>=$<span style="color: #51afef;">(</span>dirname $<span style="color: #dcaeea;">image</span><span style="color: #51afef;">)</span>
python -m table_ocr.ocr_to_csv $<span style="color: #51afef;">(</span><span style="color: #ECBE7B;">find</span> $<span style="color: #dcaeea;">dir</span>/cells -name <span style="color: #98be65;">"*.txt"</span><span style="color: #51afef;">)</span>
<span style="color: #51afef;">done</span>
</pre>
</div>
<p>
Any extra args you pass after the image path to <code>python -m table_ocr.ocr_image</code> will be passed directly to tesseract as options. If you don’t pass anything, reasonable english defaults are used.
</p>
</div>
</div>
<div id="outline-container-org7e5cd11" class="outline-3">
<h3 id="org7e5cd11"><span class="section-number-3">1.4</span> Possible improvements</h3>
<div class="outline-text-3" id="text-1-4">
<p>
Detect text with the stroke-width-transform alogoritm. <a href="https://zablo.net/blog/post/stroke-width-transform-swt-python/index.html">https://zablo.net/blog/post/stroke-width-transform-swt-python/index.html</a>
</p>
</div>
</div>
</div>
<div id="outline-container-org51af43b" class="outline-2">
<h2 id="org51af43b"><span class="section-number-2">2</span> Preparing data</h2>
<div class="outline-text-2" id="text-2">
<p>
Not all pdfs need to be sent through OCR to extract the text content. If you can
click and drag to highlight text in the pdf, then the tools in this library
probably aren’t necessary.
</p>
</div>
<div id="outline-container-orga4dde96" class="outline-3">
<h3 id="orga4dde96"><span class="section-number-3">2.1</span> Converting PDFs to images</h3>
<div class="outline-text-3" id="text-2-1">
<p>
This code calls out to <a href="https://manpages.debian.org/testing/poppler-utils/pdfimages.1.en.html">pdfimages</a> from <a href="https://poppler.freedesktop.org/">Poppler</a>.
</p>
<div class="org-src-container">
<pre class="src src-python" id="orgdb8901b"><span style="color: #5B6268;"># </span><span style="color: #5B6268;">Wrapper around the Poppler command line utility "pdfimages" and helpers for</span>
<span style="color: #5B6268;"># </span><span style="color: #5B6268;">finding the output files of that command.</span>
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">pdf_to_images</span>(pdf_filepath):
<span style="background-color: #282c34;"> </span> <span style="color: #83898d;">"""</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> Turn a pdf into images</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> Returns the filenames of the created images sorted lexicographically.</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> """</span>
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">directory</span>, <span style="color: #dcaeea;">filename</span> = os.path.split(pdf_filepath)
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">image_filenames</span> = pdfimages(pdf_filepath)
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Since pdfimages creates a number of files named each for there page number</span>
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">and doesn't return us the list that it created</span>
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> <span style="color: #c678dd;">sorted</span>([os.path.join(directory, f) <span style="color: #51afef;">for</span> f <span style="color: #51afef;">in</span> image_filenames])
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">pdfimages</span>(pdf_filepath):
<span style="background-color: #282c34;"> </span> <span style="color: #83898d;">"""</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> Uses the `pdfimages` utility from Poppler</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> (https://poppler.freedesktop.org/). Creates images out of each page. Images</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> are prefixed by their name sans extension and suffixed by their page number.</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> This should work up to pdfs with 999 pages since find matching files in dir</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> uses 3 digits in its regex.</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> """</span>
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">directory</span>, <span style="color: #dcaeea;">filename</span> = os.path.split(pdf_filepath)
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">if</span> <span style="color: #51afef;">not</span> os.path.isabs(directory):
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">directory</span> = os.path.abspath(directory)
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">filename_sans_ext</span> = filename.split(<span style="color: #98be65;">".pdf"</span>)[<span style="color: #da8548; font-weight: bold;">0</span>]
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">pdfimages outputs results to the current working directory</span>
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">with</span> working_dir(directory):
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> subprocess.run([<span style="color: #98be65;">"pdfimages"</span>, <span style="color: #98be65;">"-png"</span>, filename, filename.split(<span style="color: #98be65;">".pdf"</span>)[<span style="color: #da8548; font-weight: bold;">0</span>]])
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">image_filenames</span> = find_matching_files_in_dir(filename_sans_ext, directory)
<span style="background-color: #282c34;"> </span> logger.debug(
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #98be65;">"Converted {} into files:\n{}"</span>.<span style="color: #c678dd;">format</span>(pdf_filepath, <span style="color: #98be65;">"\n"</span>.join(image_filenames))
<span style="background-color: #282c34;"> </span> )
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> image_filenames
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">find_matching_files_in_dir</span>(file_prefix, directory):
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">files</span> = [
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> filename
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">for</span> filename <span style="color: #51afef;">in</span> os.listdir(directory)
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #51afef;">if</span> re.match(r<span style="color: #98be65;">"{}-\d{{3}}.*\.png"</span>.<span style="color: #c678dd;">format</span>(re.escape(file_prefix)), filename)
<span style="background-color: #282c34;"> </span> ]
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> files
</pre>
</div>
</div>
</div>
<div id="outline-container-org6c75ffa" class="outline-3">
<h3 id="org6c75ffa"><span class="section-number-3">2.2</span> Detecting image orientation and applying rotation.</h3>
<div class="outline-text-3" id="text-2-2">
<p>
Tesseract can detect orientation and we can then use <a href="https://www.imagemagick.org/script/mogrify.php">ImageMagick’s mogrify</a> to
rotate the image.
</p>
<p>
Here’s an example of the output we get from orientation detection with
Tesseract.
</p>
<pre class="example">
➜ example/ tesseract --psm 0 example-000.png -
Page number: 0
Orientation in degrees: 90
Rotate: 270
Orientation confidence: 26.86
Script: Latin
Script confidence: 2.44
</pre>
<p>
The following are some helpers to detect orientation of the images that Poppler
extracted and, if the images are rotated or skewed, use ImageMagick’s `mogrify`
to correct the rotation. This makes OCR more straightforward.
</p>
<div class="org-src-container">
<pre class="src src-python" id="org44f8315"><span style="color: #51afef;">def</span> <span style="color: #c678dd;">preprocess_img</span>(filepath, tess_params=<span style="color: #a9a1e1;">None</span>):
<span style="background-color: #282c34;"> </span> <span style="color: #83898d;">"""Processing that involves running shell executables,</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> like mogrify to rotate.</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> Uses tesseract to detect rotation.</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> </span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> Orientation and script detection is only available for legacy tesseract</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> (--oem 0). Some versions of tesseract will segfault if you let it run OSD</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> with the default oem (3).</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> """</span>
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">if</span> tess_params <span style="color: #51afef;">is</span> <span style="color: #a9a1e1;">None</span>:
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">tess_params</span> = [<span style="color: #98be65;">"--psm"</span>, <span style="color: #98be65;">"0"</span>, <span style="color: #98be65;">"--oem"</span>, <span style="color: #98be65;">"0"</span>]
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">rotate</span> = get_rotate(filepath, tess_params)
<span style="background-color: #282c34;"> </span> logger.debug(<span style="color: #98be65;">"Rotating {} by {}."</span>.<span style="color: #c678dd;">format</span>(filepath, rotate))
<span style="background-color: #282c34;"> </span> mogrify(filepath, rotate)
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">get_rotate</span>(image_filepath, tess_params):
<span style="background-color: #282c34;"> </span> <span style="color: #83898d;">"""</span>
<span style="color: #83898d; background-color: #282c34;"> </span><span style="color: #83898d;"> """</span>
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">tess_command</span> = [<span style="color: #98be65;">"tesseract"</span>] + tess_params + [image_filepath, <span style="color: #98be65;">"-"</span>]
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">output</span> = (
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> subprocess.check_output(tess_command)
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> .decode(<span style="color: #98be65;">"utf-8"</span>)
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> .split(<span style="color: #98be65;">"\n"</span>)
<span style="background-color: #282c34;"> </span> )
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">output</span> = <span style="color: #c678dd;">next</span>(l <span style="color: #51afef;">for</span> l <span style="color: #51afef;">in</span> output <span style="color: #51afef;">if</span> <span style="color: #98be65;">"Rotate: "</span> <span style="color: #51afef;">in</span> l)
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">output</span> = output.split(<span style="color: #98be65;">": "</span>)[<span style="color: #da8548; font-weight: bold;">1</span>]
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> output
<span style="color: #51afef;">def</span> <span style="color: #c678dd;">mogrify</span>(image_filepath, rotate):
<span style="background-color: #282c34;"> </span> subprocess.run([<span style="color: #98be65;">"mogrify"</span>, <span style="color: #98be65;">"-rotate"</span>, rotate, image_filepath])
</pre>
</div>
</div>
</div>
</div>
<div id="outline-container-orgc195fec" class="outline-2">
<h2 id="orgc195fec"><span class="section-number-2">3</span> Detecting tables</h2>
<div class="outline-text-2" id="text-3">
<p>
This answer from opencv.org was heavily referenced while writing the code around
table detection:
<a href="https://answers.opencv.org/question/63847/how-to-extract-tables-from-an-image/">https://answers.opencv.org/question/63847/how-to-extract-tables-from-an-image/</a>.
</p>
<p>
It’s much easier to OCR a table when the table is the only thing in the image.
This code detects tables in an image and returns a list of images of just the
tables, no surrounding text or noise.
</p>
<p>
The blurring, thresholding, and line detection is used here as well as later on
for cell extraction. They are good techniques for cleaning an image up in a way
that makes things like shape detection more accurate.
</p>
<div class="org-src-container">
<pre class="src src-python" id="orgd821c1d"><span style="color: #51afef;">def</span> <span style="color: #c678dd;">find_tables</span>(image):
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">BLUR_KERNEL_SIZE</span> = (<span style="color: #da8548; font-weight: bold;">17</span>, <span style="color: #da8548; font-weight: bold;">17</span>)
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">STD_DEV_X_DIRECTION</span> = <span style="color: #da8548; font-weight: bold;">0</span>
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">STD_DEV_Y_DIRECTION</span> = <span style="color: #da8548; font-weight: bold;">0</span>
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">blurred</span> = cv2.GaussianBlur(image, BLUR_KERNEL_SIZE, STD_DEV_X_DIRECTION, STD_DEV_Y_DIRECTION)
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">MAX_COLOR_VAL</span> = <span style="color: #da8548; font-weight: bold;">255</span>
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">BLOCK_SIZE</span> = <span style="color: #da8548; font-weight: bold;">15</span>
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">SUBTRACT_FROM_MEAN</span> = -<span style="color: #da8548; font-weight: bold;">2</span>
<span style="background-color: #282c34;"> </span>
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">img_bin</span> = cv2.adaptiveThreshold(
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> ~blurred,
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> MAX_COLOR_VAL,
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cv2.ADAPTIVE_THRESH_MEAN_C,
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> cv2.THRESH_BINARY,
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> BLOCK_SIZE,
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> SUBTRACT_FROM_MEAN,
<span style="background-color: #282c34;"> </span> )
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertical</span> = <span style="color: #dcaeea;">horizontal</span> = img_bin.copy()
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">SCALE</span> = <span style="color: #da8548; font-weight: bold;">5</span>
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">image_width</span>, <span style="color: #dcaeea;">image_height</span> = horizontal.shape
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">horizontal_kernel</span> = cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #c678dd;">int</span>(image_width / SCALE), <span style="color: #da8548; font-weight: bold;">1</span>))
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">horizontally_opened</span> = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel)
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertical_kernel</span> = cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">1</span>, <span style="color: #c678dd;">int</span>(image_height / SCALE)))
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertically_opened</span> = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel)
<span style="background-color: #282c34;"> </span>
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">horizontally_dilated</span> = cv2.dilate(horizontally_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">40</span>, <span style="color: #da8548; font-weight: bold;">1</span>)))
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">vertically_dilated</span> = cv2.dilate(vertically_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">1</span>, <span style="color: #da8548; font-weight: bold;">60</span>)))
<span style="background-color: #282c34;"> </span>
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">mask</span> = horizontally_dilated + vertically_dilated
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">contours</span>, <span style="color: #dcaeea;">heirarchy</span> = cv2.findContours(
<span style="background-color: #282c34;"> </span> <span style="background-color: #282c34;"> </span> mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE,
<span style="background-color: #282c34;"> </span> )
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">MIN_TABLE_AREA</span> = 1e5
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">contours</span> = [c <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> contours <span style="color: #51afef;">if</span> cv2.contourArea(c) > MIN_TABLE_AREA]
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">perimeter_lengths</span> = [cv2.arcLength(c, <span style="color: #a9a1e1;">True</span>) <span style="color: #51afef;">for</span> c <span style="color: #51afef;">in</span> contours]
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">epsilons</span> = [<span style="color: #da8548; font-weight: bold;">0.1</span> * p <span style="color: #51afef;">for</span> p <span style="color: #51afef;">in</span> perimeter_lengths]
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">approx_polys</span> = [cv2.approxPolyDP(c, e, <span style="color: #a9a1e1;">True</span>) <span style="color: #51afef;">for</span> c, e <span style="color: #51afef;">in</span> <span style="color: #c678dd;">zip</span>(contours, epsilons)]
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">bounding_rects</span> = [cv2.boundingRect(a) <span style="color: #51afef;">for</span> a <span style="color: #51afef;">in</span> approx_polys]
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">The link where a lot of this code was borrowed from recommends an</span>
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">additional step to check the number of "joints" inside this bounding rectangle.</span>
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">A table should have a lot of intersections. We might have a rectangular image</span>
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">here though which would only have 4 intersections, 1 at each corner.</span>
<span style="background-color: #282c34;"> </span> <span style="color: #5B6268;"># </span><span style="color: #5B6268;">Leaving that step as a future </span><span style="color: #ECBE7B; font-weight: bold;">TODO</span><span style="color: #5B6268;"> if it is ever necessary.</span>
<span style="background-color: #282c34;"> </span> <span style="color: #dcaeea;">images</span> = [image[y:y+h, x:x+w] <span style="color: #51afef;">for</span> x, y, w, h <span style="color: #51afef;">in</span> bounding_rects]
<span style="background-color: #282c34;"> </span> <span style="color: #51afef;">return</span> images
</pre>
</div>
<p>
Here is an the an example of the result of the <code>find_tables</code> function.
</p>
<div class="org-src-container">
<pre class="src src-python"><span style="color: #51afef;">import</span> cv2
<span style="color: #dcaeea;">image_filename</span> = <span style="color: #98be65;">"resources/examples/example-page.png"</span>
<span style="color: #dcaeea;">image</span> = cv2.imread(image_filename, cv2.IMREAD_GRAYSCALE)
<span style="color: #dcaeea;">image</span> = find_tables(image)[<span style="color: #da8548; font-weight: bold;">0</span>]
cv2.imwrite(<span style="color: #98be65;">"resources/examples/example-table.png"</span>, image)
</pre>
</div>
<div class="org-center">
<div class="figure">
<p><img src="resources/examples/example-page.png" alt="example-page.png" width="250px" />
</p>
</div>
<p>
↓
</p>
<div class="figure">
<p><img src="resources/examples/example-table.png" alt="example-table.png" width="250px" />
</p>
</div>
</div>
</div>
<div id="outline-container-org9dd75a6" class="outline-3">
<h3 id="org9dd75a6"><span class="section-number-3">3.1</span> Improving accuracy</h3>
<div class="outline-text-3" id="text-3-1">
<p>
It’s likely that some images will contain tables that aren’t accurately
recognized by the code above. The code will then need to be made more robust.
But how will we know that changes to the code don’t break the detection of
tables that were previously detected?
</p>
<p>
It might be good to add some type of test suite in the future that contains a
spec that matches a pdf with the pages and pixel coordinates of the detected
tables. The coordinates would need to have a range. Something like
“example-1.pdf, page-2.png, [450:470, 200:210, 800:820, 1270:1290]” where the
elements of the list are valid x, y, w, h ranges. So the test will pass if if
the x, y, width and height are anywhere in that range.
</p>
</div>
</div>
</div>
<div id="outline-container-org904debc" class="outline-2">
<h2 id="org904debc"><span class="section-number-2">4</span> OCR tables</h2>
<div class="outline-text-2" id="text-4">
<p>
Tesseract does not perform well when run on images of tables. It performs best
when given a single line of text with no extra noise.
</p>
<p>
Therefore, our next task is to find and extract the bounding box of each cell in
the table. Run tesseract on each cell. Print a comma seperated output.
</p>
<p>
We’ll start with an image shown at the end of the previous section.
</p>
</div>
<div id="outline-container-orgb03a965" class="outline-3">
<h3 id="orgb03a965"><span class="section-number-3">4.1</span> Training Tesseract</h3>
<div class="outline-text-3" id="text-4-1">
<p>
Tesseract is used for recognizing characters. It is not involved in extracting the tables from an image or in extracting cells from the table.
</p>
<p>
It’s a very good idea to train tesseract. Accuracy will improve tremendously.
</p>
<p>
Clone the tesstrain repo at <a href="https://github.com/tesseract-ocr/tesstrain">https://github.com/tesseract-ocr/tesstrain</a>.
</p>
<p>
Run the <a href="#org5f5c842"><code>ocr_tables</code></a> script on a few pdfs to generate some training data. That
script outputs pairs of <code>.png</code> and <code>.gt.txt</code> files that can be used by
tesstrain.
</p>
<p>
Make sure the <code>.gt.txt</code> files contain an accurate recognition of the
corresponding image. Since the first few runs will be untrained, you’ll probably
need to fix up a few of the text files.
</p>
<p>
Once they are accurate, move them to a new subdirectory of the tesstrain repo;
<code>tesstrain/data/<model-name>-ground-truth/</code>.
</p>
<p>
You’ll also need to clone the <code>tessdata_best</code> repo,
<a href="https://github.com/tesseract-ocr/tessdata_best">https://github.com/tesseract-ocr/tessdata_best</a> and the
<a href="https://github.com/tesseract-ocr/langdata">https://github.com/tesseract-ocr/langdata</a> to use as the start of the
training model.
</p>
<p>
I’m actually not sure how much the punctuation and numbers from <code>langdata</code> help.
I didn’t keep accurate records while playing with the training, I don’t
thoroughly understand it, and it’s not profitable for me to explore it at the
moment. It worked for my purposes and that has been good enough.
</p>
<pre class="example">
make training MODEL_NAME=table-ocr START_MODEL=eng TESSDATA=~/src/tessdata_best PUNC_FILE=~/src/langdata/eng/eng.punc NUMBERS_FILE=~/src/langdata/eng/eng.numbers
</pre>
<p>
Once the training is complete, there will be a new file
<code>tesstrain/data/<model-name>.traineddata</code>. Copy that file to the directory
Tesseract searches for models. On my machine, it was <code>/usr/local/share/tessdata/</code>.
</p>
</div>
<div id="outline-container-org5adeb27" class="outline-4">
<h4 id="org5adeb27"><span class="section-number-4">4.1.1</span> Training tips</h4>
<div class="outline-text-4" id="text-4-1-1">
<p>
Here is a tip for quickly creating training data.
</p>
<p>
The output of the <code>ocr_cells</code> script will be a directory named <code>ocr_data</code> that
will have two files for each cell. One file is the image of the cell and the
other file is the OCR text.
</p>
<p>
You’ll want to compare each image to its OCR text to check for accuracy. If
the text doesn’t match, you’ll want to update the text and add the image to the
training data.
</p>
<p>
The fastest way to do this is with <code>feh</code>.
</p>
<p>
<code>feh</code> lets you view an image and a caption at the same time and lets you edit
the caption from within <code>feh</code>.
</p>
<p>
<code>feh</code> expects the captions to be named <code><image-name>.txt</code>, so use a little
shell-fu to do a quick rename.
</p>
<div class="org-src-container">
<pre class="src src-shell"><span style="color: #51afef;">for</span> f<span style="color: #51afef;"> in</span> *.txt; <span style="color: #51afef;">do</span> <span style="color: #dcaeea;">f1</span>=$<span style="color: #51afef;">(</span>cut -d<span style="color: #98be65;">"."</span> -f1 <<span style="color: #c678dd;">(</span><span style="color: #ECBE7B;">echo</span> $<span style="color: #dcaeea;">f</span><span style="color: #c678dd;">)</span><span style="color: #51afef;">)</span>; <span style="color: #ECBE7B;">mv</span> $<span style="color: #dcaeea;">f</span> $<span style="color: #51afef;">{</span><span style="color: #dcaeea;">f1</span><span style="color: #51afef;">}</span>.png.txt; <span style="color: #51afef;">done</span>
</pre>
</div>
<p>
Then run <code>feh -K .</code> to specify the current directory as the caption directory.
This will open a window with the first image in the directory and its caption.
</p>
<p>
Press <code>c</code> to edit the caption (if needed) and <code>n~/~p</code> to move to the
next/previons images. Press <code>q</code> to quit.
</p>
<p>
When finished, rename the files back to the filename structure that Tesseract
looks for in training.
</p>
<div class="org-src-container">
<pre class="src src-shell"><span style="color: #51afef;">for</span> f<span style="color: #51afef;"> in</span> *.txt; <span style="color: #51afef;">do</span> <span style="color: #dcaeea;">f1</span>=$<span style="color: #51afef;">(</span>cut -d<span style="color: #98be65;">"."</span> -f1 <<span style="color: #c678dd;">(</span><span style="color: #ECBE7B;">echo</span> $<span style="color: #dcaeea;">f</span><span style="color: #c678dd;">)</span><span style="color: #51afef;">)</span>; <span style="color: #ECBE7B;">mv</span> $<span style="color: #dcaeea;">f</span> $<span style="color: #51afef;">{</span><span style="color: #dcaeea;">f1</span><span style="color: #51afef;">}</span>.gt.txt; <span style="color: #51afef;">done</span>
</pre>
</div>
</div>
</div>
</div>
<div id="outline-container-org152ead5" class="outline-3">
<h3 id="org152ead5"><span class="section-number-3">4.2</span> Blur</h3>
<div class="outline-text-3" id="text-4-2">
<p>
Blurring helps to make noise less noisy so that the overall structure of an
image is more detectable.
</p>
<p>
That gray row at the bottom is kind of noisy. If we don’t somehow clean it up,
OpenCV will detect all sorts of odd shapes in there and it will throw off our
cell detection.
</p>
<p>
Cleanup can be accomplished with a blur followed by some thresholding.
</p>
<div class="org-src-container">
<pre class="src src-python"><span style="color: #dcaeea;">BLUR_KERNEL_SIZE</span> = (<span style="color: #da8548; font-weight: bold;">17</span>, <span style="color: #da8548; font-weight: bold;">17</span>)
<span style="color: #dcaeea;">STD_DEV_X_DIRECTION</span> = <span style="color: #da8548; font-weight: bold;">0</span>
<span style="color: #dcaeea;">STD_DEV_Y_DIRECTION</span> = <span style="color: #da8548; font-weight: bold;">0</span>
<span style="color: #dcaeea;">blurred</span> = cv2.GaussianBlur(image, BLUR_KERNEL_SIZE, STD_DEV_X_DIRECTION, STD_DEV_Y_DIRECTION)
</pre>
</div>
<div class="org-src-container">
<pre class="src src-python"><span style="color: #dcaeea;">image</span> = ~cv2.imread(<span style="color: #98be65;">"resources/examples/example-table.png"</span>, cv2.IMREAD_GRAYSCALE)
<<blur>>
cv2.imwrite(<span style="color: #98be65;">"resources/examples/example-table-blurred.png"</span>, blurred)
</pre>
</div>
<div class="figure">
<p><img src="resources/examples/example-table-blurred.png" alt="example-table-blurred.png" width="500px" height="100%" />
</p>
</div>
</div>
</div>
<div id="outline-container-org858fb89" class="outline-3">
<h3 id="org858fb89"><span class="section-number-3">4.3</span> Threshold</h3>
<div class="outline-text-3" id="text-4-3">
<p>
We’ve got a bunch of pixels that are gray. Thresholding will turn them all
either black or white. Having all black or white pixels lets us do morphological
transformations like erosion and dilation.
</p>
<div class="org-src-container">
<pre class="src src-python"><span style="color: #dcaeea;">MAX_COLOR_VAL</span> = <span style="color: #da8548; font-weight: bold;">255</span>
<span style="color: #dcaeea;">BLOCK_SIZE</span> = <span style="color: #da8548; font-weight: bold;">15</span>
<span style="color: #dcaeea;">SUBTRACT_FROM_MEAN</span> = -<span style="color: #da8548; font-weight: bold;">2</span>
<span style="color: #dcaeea;">img_bin</span> = cv2.adaptiveThreshold(
<span style="background-color: #282c34;"> </span> ~blurred,
<span style="background-color: #282c34;"> </span> MAX_COLOR_VAL,
<span style="background-color: #282c34;"> </span> cv2.ADAPTIVE_THRESH_MEAN_C,
<span style="background-color: #282c34;"> </span> cv2.THRESH_BINARY,
<span style="background-color: #282c34;"> </span> BLOCK_SIZE,
<span style="background-color: #282c34;"> </span> SUBTRACT_FROM_MEAN,
)
</pre>
</div>
<div class="org-src-container">
<pre class="src src-python"><<threshold>>
cv2.imwrite(<span style="color: #98be65;">"resources/examples/example-table-thresholded.png"</span>, img_bin)
</pre>
</div>
<div class="figure">
<p><img src="resources/examples/example-table-thresholded.png" alt="example-table-thresholded.png" width="500px" height="100%" />
</p>
</div>
</div>
</div>
<div id="outline-container-orgcf17042" class="outline-3">
<h3 id="orgcf17042"><span class="section-number-3">4.4</span> Finding the vertical and horizontal lines of the table</h3>
<div class="outline-text-3" id="text-4-4">
<div class="org-src-container">
<pre class="src src-python"><span style="color: #dcaeea;">vertical</span> = <span style="color: #dcaeea;">horizontal</span> = img_bin.copy()
<span style="color: #dcaeea;">SCALE</span> = <span style="color: #da8548; font-weight: bold;">5</span>
<span style="color: #dcaeea;">image_width</span>, <span style="color: #dcaeea;">image_height</span> = horizontal.shape
<span style="color: #dcaeea;">horizontal_kernel</span> = cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #c678dd;">int</span>(image_width / SCALE), <span style="color: #da8548; font-weight: bold;">1</span>))
<span style="color: #dcaeea;">horizontally_opened</span> = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, horizontal_kernel)
<span style="color: #dcaeea;">vertical_kernel</span> = cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">1</span>, <span style="color: #c678dd;">int</span>(image_height / SCALE)))
<span style="color: #dcaeea;">vertically_opened</span> = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, vertical_kernel)
<span style="color: #dcaeea;">horizontally_dilated</span> = cv2.dilate(horizontally_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">40</span>, <span style="color: #da8548; font-weight: bold;">1</span>)))
<span style="color: #dcaeea;">vertically_dilated</span> = cv2.dilate(vertically_opened, cv2.getStructuringElement(cv2.MORPH_RECT, (<span style="color: #da8548; font-weight: bold;">1</span>, <span style="color: #da8548; font-weight: bold;">60</span>)))
<span style="color: #dcaeea;">mask</span> = horizontally_dilated + vertically_dilated
</pre>
</div>
<p>
Note: There’s a wierd issue with the results of the example below when it’s
evaluated as part of an export or a full-buffer evaluation. If you evaluate the
example by itself, it looks the way it’s intended. If you evaluate it as part of
an entire buffer evaluation, like during export, it’s distorted.
</p>
<div class="org-src-container">
<pre class="src src-python"><<lines-of-table>>
cv2.imwrite(<span style="color: #98be65;">"resources/examples/example-table-lines.png"</span>, mask)
</pre>
</div>
<div class="figure">
<p><img src="resources/examples/example-table-lines.png" alt="example-table-lines.png" width="500px" />
</p>
</div>
</div>
</div>
<div id="outline-container-org94f71b3" class="outline-3">
<h3 id="org94f71b3"><span class="section-number-3">4.5</span> Finding the contours</h3>
<div class="outline-text-3" id="text-4-5">
<p>
Blurring and thresholding allow us to find the lines. Opening the lines allows
us to find the contours.
</p>
<p>
An “Opening” is an erosion followed by a dilation. Great examples and
descriptions of each morphological operation can be found at
<a href="https://docs.opencv.org/trunk/d9/d61/tutorial_py_morphological_ops.html">https://docs.opencv.org/trunk/d9/d61/tutorial_py_morphological_ops.html</a>.
</p>
<blockquote>
<p>
Contours can be explained simply as a curve joining all the continuous points