PyLaia - HOME-Alcar

This model performs Handwritten Text Recognition in Latin on medieval documents.

Model description

The model was trained using the PyLaia library on two medieval datasets:

Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio.

An external 6-gram character language model can be used to improve recognition. The language model is trained on the text from the HOME-Alcar training set.

Evaluation results

On HOME-Alcar text lines, the model achieves the following results:

set Language model CER (%) WER (%) lines
test no 8.35 26.15 6,932
test yes 7.85 23.20 6,932

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}
}
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Dataset used to train Teklia/pylaia-home-alcar

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