PyLaia - IAM
This model performs Handwritten Text Recognition in English on modern documents.
Model description
The model was trained using the PyLaia library on the RWTH split of the IAM dataset.
Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio.
| set | lines |
|---|---|
| train | 6,482 |
| val | 976 |
| test | 2,915 |
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.
Evaluation results
The model achieves the following results:
| set | Language model | CER (%) | WER (%) | lines |
|---|---|---|---|---|
| test | no | 8.44 | 24.51 | 2,915 |
| test | yes | 7.50 | 20.98 | 2,915 |
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}
}