Teklia/pylaia-belfort

PyLaia - Belfort

This model performs Handwritten Text Recognition in French on historical documents.

Model description

The model was trained using the PyLaia library on the Belfort dataset.

For training, text-lines were resized with a fixed height of 128 pixels, keeping the original aspect ratio. Vertical lines are discarded.

split	N lines
train	25,800
val	3,102
test	3,819

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

Evaluation results

The model achieves the following results:

set	Language model	CER (%)	WER (%)	N lines
test	no	10.54	28.12	3,819
test	yes	9.52	23.73	3,819

How to use?

Please refer to the documentation.

Cite us!

@inproceedings{pylaia-lib,
    author = "Tarride, Solène and Schneider, Yoann and Generali, Marie and Boillet, Melodie and Abadie, Bastien and Kermorvant, Christopher",
    title = "Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library",
    booktitle = "Submitted at ICDAR2024",
    year = "2024"
}

@inproceedings{belfort-2023,
    author = {Tarride, Solène and Faine, Tristan and Boillet, Mélodie and Mouchère, Harold and Kermorvant, Christopher},
    title = {Handwritten Text Recognition from Crowdsourced Annotations},
    year = {2023},
    isbn = {9798400708411},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3604951.3605517},
    doi = {10.1145/3604951.3605517},
    booktitle = {Proceedings of the 7th International Workshop on Historical Document Imaging and Processing},
    pages = {1–6},
    numpages = {6},
    keywords = {Crowdsourcing, Handwritten Text Recognition, Historical Documents, Neural Networks, Text Aggregation},
    series = {HIP '23}
}