---
library_name: PyLaia
license: mit
tags:
- PyLaia
- PyTorch
- atr
- htr
- ocr
- historical
- handwritten
metrics:
- CER
- WER
language:
- 'no'
datasets:
- Teklia/NorHand_v3
pipeline_tag: image-to-text
---

# PyLaia - NorHand v3

This model performs Handwritten Text Recognition in Norwegian. It was developed during the HUGIN-MUNIN project.

## Model description

The model has been trained using the PyLaia library on the [NorHand v3](https://zenodo.org/records/10255840) dataset.

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

| set   | lines   | horizontal lines |
|:----- | ------: | ---------------: |
| train | 224,173 |          223,971 |
| val   |  22,828 |           22,811 |
| test  |   1,573 |            1,573 |

An external 6-gram character language model can be used to improve recognition. The language model is trained on the text from the NorHand v3 training set.

## Evaluation results

The model achieves the following results:

| set   | Language model | CER (%)    | WER (%) | N lines   |
|:------|:---------------| ----------:| -------:|----------:|
| test  | no             | 7.52       |   22.99 |     1,573 |
| test  | yes            | 6.36       |   18.11 |     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}
}
```