metadata
library_name: PyLaia
license: mit
tags:
- PyLaia
- PyTorch
- Handwritten text recognition
metrics:
- CER
- WER
language:
- fr
datasets:
- Teklia/POPP
POPP handwritten text recognition
This model performs Handwritten Text Recognition on French census documents.
Model description
The model was trained using the PyLaia library on the POPP generic.
For training, text-lines were resized with a fixed height of 128 pixels, keeping the original aspect ratio.
| split | N 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 (%) | N lines |
|---|---|---|---|---|
| test | no | 16.49 | 36.26 | 479 |
| test | yes | 16.09 | 34.52 | 479 |
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"
}