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---
library_name: PyLaia
license: mit
tags:
- PyLaia
- PyTorch
- atr
- htr
- ocr
- modern
- handwritten
metrics:
- CER
language:
- zh
datasets:
- Teklia/CASIA
pipeline_tag: image-to-text
---
# PyLaia - CASIA-HWDB2
This model performs Handwritten Text Recognition in Chinese.
## Model description
The model was trained using the PyLaia library on the [CASIA-HWDB2](http://www.nlpr.ia.ac.cn/databases/handwriting/Offline_database.html) dataset.
Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio.
| set | lines |
| :----- | ------: |
| train | 33,425 |
| val | 8,325 |
| test | 10,449 |
An external 6-gram character language model can be used to improve recognition. The language model is trained on the text from the CASIA-HWDB2 training set.
## Evaluation results
The model achieves the following results:
| set | Language model | CER (%) | lines |
|:------|:---------------| ----------:|----------:|
| test | no | 4.61 | 10,449 |
| test | yes | 1.53 | 10,449 |
## 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}
}
``` |