These are worksheets for the ICDAR 2019 Tutorial on Deep Learning for Document Analysis. They implement data generation and full training for text recognition (OCR) and document segmentation using a variety of common approaches, including convolution, U-net, LSTM, 2D LSTM, transpose convolutions, and upscaling.
Please see the individual worksheets for details. The models themselves are found in ocrlib/ocrmodels.py.
Note that the worksheets checked into the repository are only partially trained (using run-all).
That is, they are only trained enough to make sure that the code works upon checkin.
For good models, train for at least 10 epochs or more.
You can run the Jupyter server with ./run-jupyter and then connect to the server at http://localhost:9888.
This will build a Docker container and then execute it as you in the current directory.
You can download the training data used with these notebooks by running `run-download.
You can generate OCR data using the word-image-generation.ipynb notebook.
You need to have a basic set of TrueType fonts installed for that.