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PyLaia - NorHand v1

This model performs Handwritten Text Recognition in Norwegian. It was developed during the HUGIN-MUNIN project.

Model description

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.

Evaluation results

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

How to use?

Please refer to the PyLaia documentation to use this model.

Cite us!

@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}
}
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