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:
- Himanis (French);
- HOME-Alcar (Latin).
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}
}