This package was created in a [[https://en.wikipedia.org/wiki/Literate_programming][literate programming]] style with the help of [[https://orgmode.org/worg/org-contrib/babel/intro.html][Babel]].
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.
Or, as a shell script.
** Example usage
Here is an example of a shell script that uses each module to turn a pdf with a
table into CSV output.
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.
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.
Detect text with the stroke-width-transform alogoritm. https://zablo.net/blog/post/stroke-width-transform-swt-python/index.html
* Preparing data
** Converting PDFs to images
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.
** Converting PDFs to images
This code calls out to [[https://manpages.debian.org/testing/poppler-utils/pdfimages.1.en.html][pdfimages]] from [[https://poppler.freedesktop.org/][Poppler]].
#+NAME: pdf-to-images
@ -228,22 +253,27 @@ def find_tables(image):
return images
#+END_SRC
Here is an the an example of the result of the ~find_tables~ function.
@ -261,11 +291,49 @@ the x, y, width and height are anywhere in that range.
* OCR tables
Find the bounding box of each cell in the table. Run tesseract on each cell.
Print a comma seperated output.
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.
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.
We'll start with an image shown at the end of the previous section.
** Training Tesseract
It's a very good idea to train tesseract. Accuracy will improve tremendously.
Clone the tesstrain repo at [[https://github.com/tesseract-ocr/tesstrain]].
Run the [[ocr_tables][~ocr_tables~]] script on a few pdfs to generate some training data. That
script outputs pairs of ~.png~ and ~.gt.txt~ files that can be used by
tesstrain.
Make sure the ~.gt.txt~ 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.
Once they are accurate, move them to a new subdirectory of the tesstrain repo;
~tesstrain/data/<model-name>-ground-truth/~.
You'll also need to clone the ~tessdata_best~ repo,
[[https://github.com/tesseract-ocr/tessdata_best]] and the
https://github.com/tesseract-ocr/langdata to use as the start of the
training model.
I'm actually not sure how much the punctuation and numbers from ~langdata~ 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.
#+BEGIN_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
#+END_EXAMPLE
Once the training is complete, there will be a new file
~tesstrain/data/<model-name>.traineddata~. Copy that file to the directory
Tesseract searches for models. On my machine, it was ~/usr/local/share/tessdata/~.
** Blur
Blurring helps to make noise less noisy so that the overall structure of an
Eric Ihli
4 years ago