README.md

metadata

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 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 to use this model.

Cite us!

@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}
}