README.md

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

# 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This model performs Handwritten Text Recognition in French on historical documents.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](https://zenodo.org/records/8041668).

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](https://atr.pages.teklia.com/pylaia/).

## Cite us


```bibtex
@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"
}
```

```bibtex
@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    booktitle = {Proceedings of the 7th International Workshop on Historical Document Imaging and Processing},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    keywords = {Crowdsourcing, Handwritten Text Recognition, Historical Documents, Neural Networks, Text Aggregation},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}
}
```