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--- |
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library_name: PyLaia |
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license: mit |
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tags: |
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- PyLaia |
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- PyTorch |
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- Handwritten text recognition |
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metrics: |
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- CER |
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- WER |
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language: |
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- fr |
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datasets: |
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- Teklia/POPP |
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--- |
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# POPP handwritten text recognition |
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This model performs Handwritten Text Recognition on French census documents. |
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## Model description |
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The model was trained using the PyLaia library on the [POPP generic](https://github.com/Shulk97/POPP-datasets/). |
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For training, text-lines were resized with a fixed height of 128 pixels, keeping the original aspect ratio. |
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An external 6-gram character language model can be used to improve recognition. The language model is trained on the text from the POPP training set. |
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## Evaluation results |
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The model achieves the following results: |
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| set | Language model | CER (%) | WER (%) | N lines | |
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|:------|:---------------|:----------:|:-------:|----------:| |
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| test | no | 16.49 | 36.26 | 479 | |
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| test | yes | 16.09 | 34.52 | 479 | |
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## How to use |
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Please refer to the [documentation](https://atr.pages.teklia.com/pylaia/). |
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## Cite us |
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```bibtex |
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@inproceedings{pylaia-lib, |
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author = "Tarride, Solène and Schneider, Yoann and Generali, Marie and Boillet, Melodie and Abadie, Bastien and Kermorvant, Christopher", |
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title = "Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library", |
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booktitle = "Submitted at ICDAR2024", |
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year = "2024" |
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} |
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``` |
