PyLaia
Collection
The PyLaia collection contains models designed for Automatic Text Recognition (ATR) from line images.
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15 items
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Updated
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3
This model performs Handwritten Text Recognition in Norwegian. It was developed during the HUGIN-MUNIN project.
The model has been trained using the PyLaia library on the NorHand v1 dataset.
Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio.
set | horizontal lines |
---|---|
train | 19,653 |
val | 2,286 |
test | 1,793 |
An external 6-gram character language model can be used to improve recognition. The language model is trained on the text from the NorHand v1 training set.
The model achieves the following results:
set | Language model | CER (%) | WER (%) | lines |
---|---|---|---|---|
test | no | 7.94 | 24.04 | 1,793 |
test | yes | 6.55 | 18.20 | 1,793 |
Please refer to the PyLaia documentation to use this model.
@inproceedings{pylaia2024,
author = {Tarride, Solène and Schneider, Yoann and Generali-Lince, Marie and Boillet, Mélodie and Abadie, Bastien and Kermorvant, Christopher},
title = {{Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library}},
booktitle = {Document Analysis and Recognition - ICDAR 2024},
year = {2024},
publisher = {Springer Nature Switzerland},
address = {Cham},
pages = {387--404},
isbn = {978-3-031-70549-6}
}
@inproceedings{10.1007/978-3-031-06555-2_27,
author = {Maarand, Martin and Beyer, Yngvil and K\r{a}sen, Andre and Fosseide, Knut T. and Kermorvant, Christopher},
title = {A Comprehensive Comparison of Open-Source Libraries for Handwritten Text Recognition in Norwegian},
year = {2022},
isbn = {978-3-031-06554-5},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-06555-2_27},
doi = {10.1007/978-3-031-06555-2_27},
booktitle = {Document Analysis Systems: 15th IAPR International Workshop, DAS 2022, La Rochelle, France, May 22–25, 2022, Proceedings},
pages = {399–413},
numpages = {15},
keywords = {Norwegian language, Open-source, Handwriting recognition},
location = {La Rochelle, France}
}