---
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](https://github.com/Shulk97/POPP-datasets/).

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](https://atr.pages.teklia.com/pylaia/usage/prediction/) to use this model.

## Cite us!

```bibtex
@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}
}
```