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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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- en |
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datasets: |
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- Teklia/IAM |
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--- |
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# IAM handwritten text recognition |
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This model performs Handwritten Text Recognition in English on modern documents. |
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## Model description |
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The model was trained using the PyLaia library on the [IAM database](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database). |
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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 IAM 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 | 8.44 | 24.51 | 2915 | |
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| test | yes | 7.50 | 20.98 | 2915 | |
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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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``` |
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