I did use another data source from Manheim but can't locate it right now.
Using vanilla Ubuntu 18.04
I looked at the example training files and made a small script to convert my own labeled data to fit the format that tesseract requires.
I did do a bit of pre-processing adjusting contrast.
All the data munging was done on Python (Pillow for image processing, Flask for collecting data into a simple SQLite DB before converting back to format that Tesseract requires).
Python was not necessary just something that felt most comfortable to me. I am sure someone could do it using bash scripts or node.js or anything else.
EDIT: To make life easier for my curators I did run Tesseract first to generate prelabeled data for my training set. It was about 90% accurate to start with.
So the process was:
Tesseract OCR on some documents to be trained -> hand curation (2 months)-> train (took about 12 hours) -> 99% (on completely separate test set)
Also this: https://github.com/UB-Mannheim/tesseract/wiki
The original data was here: https://github.com/tesseract-ocr/langdata_lstm
I did use another data source from Manheim but can't locate it right now.
Using vanilla Ubuntu 18.04
I looked at the example training files and made a small script to convert my own labeled data to fit the format that tesseract requires.
I did do a bit of pre-processing adjusting contrast.
All the data munging was done on Python (Pillow for image processing, Flask for collecting data into a simple SQLite DB before converting back to format that Tesseract requires).
Python was not necessary just something that felt most comfortable to me. I am sure someone could do it using bash scripts or node.js or anything else.
EDIT: To make life easier for my curators I did run Tesseract first to generate prelabeled data for my training set. It was about 90% accurate to start with.
So the process was: Tesseract OCR on some documents to be trained -> hand curation (2 months)-> train (took about 12 hours) -> 99% (on completely separate test set)