|
--- |
|
library_name: PyLaia |
|
license: mit |
|
tags: |
|
- PyLaia |
|
- PyTorch |
|
- atr |
|
- htr |
|
- ocr |
|
- historical |
|
- handwritten |
|
metrics: |
|
- CER |
|
- WER |
|
language: |
|
- ca |
|
datasets: |
|
- Teklia/esposalles |
|
pipeline_tag: image-to-text |
|
--- |
|
|
|
# PyLaia - Esposalles |
|
|
|
This model performs Handwritten Text Recognition in Catalan (19th century). |
|
|
|
## Model description |
|
|
|
The model has been trained using the PyLaia library on the [Esposalles](https://rrc.cvc.uab.es/?ch=10&com=introduction/) dataset. |
|
|
|
Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio. |
|
|
|
| set | lines | |
|
| :----- | ------: | |
|
| train | 2,328 | |
|
| val | 742 | |
|
| test | 757 | |
|
|
|
An external 6-gram character language model can be used to improve recognition. The language model is trained on the text from the Esposalles training set. |
|
|
|
## Evaluation results |
|
|
|
The model achieves the following results: |
|
|
|
| set | Language model | CER (%) | WER (%) | lines | |
|
|:------|:---------------| ----------:| -------:|----------:| |
|
| test | no | 0.76 | 2.62 | 757 | |
|
| test | yes | 1.04 | 3.38 | 757 | |
|
|
|
## 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} |
|
} |
|
``` |
