---
library_name: PyLaia
license: mit
tags:
- PyLaia
- PyTorch
- Handwritten text recognition
metrics:
- CER
- WER
language:
- 'lat'
---

# HOME-Alcar and Himanis handwritten text recognition

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

## Model description

The model has been trained using the PyLaia library on the [NorHand](https://zenodo.org/record/5600884) document images.
Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio.

## Evaluation results

The model achieves the following results:

Himanis:

| set   | CER (%)    | WER (%)   | support   |
| ----- | ---------- | --------- | --------- |
| train | 5.31       | 17.47     |   18503   |
| val   | 10.37      | 27.63     |    2367   |
| test  | 9.87       | 28.27     |    2241   |


Alcar:

| set   | CER (%)    | WER (%)   | support   |
| ----- | ---------- | --------- | --------- |
| train | 4.74       | 17.29     |   59969   |
| val   | 7.82       | 23.67     |    7905   |
| test  | 8.34       | 24.57     |    6932   |

## How to use

Please refer to the PyLaia library page (https://pypi.org/project/pylaia/) to use this model.

# Cite us!

```bibtex
@inproceedings{10.1007/978-3-031-06555-2_27,
author = {Maarand, Martin and Beyer, Yngvil and K\r{a}sen, Andre and Fosseide, Knut T. and Kermorvant, Christopher},
title = {A Comprehensive Comparison of Open-Source Libraries for Handwritten Text Recognition in Latin},
year = {2022},
isbn = {978-3-031-06554-5},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-06555-2_27},
doi = {10.1007/978-3-031-06555-2_27},
booktitle = {Document Analysis Systems: 15th IAPR International Workshop, DAS 2022, La Rochelle, France, May 22–25, 2022, Proceedings},
pages = {399–413},
numpages = {15},
keywords = {Latin language, Open-source, Handwriting recognition},
location = {La Rochelle, France}
}
```