The setup for running tesstrain.sh is the same as for base Tesseract. Use --linedata_only
option for LSTM training. Note that it is beneficial to have more training text and make more pages though, as neural nets don't generalize as well and need to train on something similar to what they will be running on. If the target domain is severely limited, then all the dire warnings about needing a lot of training data may not apply, but the network specification may need to be changed.
Training data is created using tesstrain.sh as follows: Note that your fonts location may vary.
training/tesstrain.sh --fonts_dir /usr/share/fonts --lang eng --linedata_only \
--noextract_font_properties --langdata_dir ../langdata \
--tessdata_dir ./tessdata --output_dir ~/tesstutorial/engtrain
The above command makes LSTM training data equivalent to the data used to train base Tesseract for English. For making a general-purpose LSTM-based OCR engine, it is woefully inadequate, but makes a good tutorial demo.
Now try this to make eval data for the 'Impact' font:
training/tesstrain.sh --fonts_dir /usr/share/fonts --lang eng --linedata_only \
--noextract_font_properties --langdata_dir ../langdata \
--tessdata_dir ./tessdata \
--fontlist "Impact Condensed" --output_dir ~/tesstutorial/engeval"--
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