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

# PyLaia - HOME-Alcar

This model performs Handwritten Text Recognition in Latin on medieval documents.

## Model description

The model was trained using the PyLaia library on two medieval datasets:
* [Himanis](https://demo.arkindex.org/browse/5000e248-a624-4df1-8679-1b34679817ef?top_level=true&folder=true) (French);
* [HOME-Alcar](https://demo.arkindex.org/browse/46b9b1f4-baeb-4342-a501-e2f15472a276?top_level=true&folder=true) (Latin).

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

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

## Evaluation results

On HOME-Alcar text lines, the model achieves the following results:

| set   | Language model | CER (%)    | WER (%) | lines   |
|:------|:---------------| ----------:| -------:|----------:|
| test  | no             | 8.35       | 26.15   |     6,932 |
| test  | yes            | 7.85       | 23.20   |     6,932 |

## 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}
}
```