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}
}
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Dataset used to train Teklia/pylaia-casia-hwdb2

Collection including Teklia/pylaia-casia-hwdb2