README.md

metadata

library_name: PyLaia
license: mit
tags:
  - PyLaia
  - PyTorch
  - atr
  - htr
  - ocr
  - historical
  - handwritten
metrics:
  - CER
  - WER
language:
  - fr
datasets:
  - Teklia/POPP
pipeline_tag: image-to-text

PyLaia - POPP

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

Model description

The model was trained using the PyLaia library on the POPP generic dataset.

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

set	lines
train	3,835
val	480
test	479

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

Evaluation results

The model achieves the following results:

set	Language model	CER (%)	WER (%)	lines
test	no	16.49	36.26	479
test	yes	16.09	34.52	479

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}
}