README.md

---
library_name: PyLaia
license: mit
tags:
- PyLaia
- PyTorch
- atr
- htr
- ocr
- historical
- handwritten
metrics:
- CER
- WER
language:
- 'no'
datasets:
- Teklia/NorHand_v2
pipeline_tag: image-to-text
---

# PyLaia - NorHand v2

This model performs Handwritten Text Recognition in Norwegian. It was developed during the [HUGIN-MUNIN project](https://hugin-munin-project.github.io/).

## Model description

The model was trained using the PyLaia library on the [NorHand v2](https://zenodo.org/records/10555698) dataset.

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

| set   | lines   | horizontal lines |
| :---- | ------: | ---------------: |
| train | 146,693 |          145,061 |
| val   |  15,119 |           14,980 |
| test  |   1,830 |            1,793 |

An external 6-gram character language model can be used to improve recognition. The language model is trained on the text from the NorHand v2 training set.

## Evaluation results

The model achieves the following results:

| set   | Language model | CER (%)    | WER (%) |   lines   |
|:------|:---------------| ----------:| -------:|----------:|
| test  | no             | 3.82       |   12.00 |     1,573 |
| test  | yes            | 3.13       |    9.29 |     1,573 |

## 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}
}
```