metadata

library_name: PyLaia
license: mit
tags:
  - PyLaia
  - PyTorch
  - Handwritten text recognition
metrics:
  - CER
  - WER
language:
  - 'no'
datasets:
  - Teklia/NorHand_v1

NorHand v1 Handwritten Text Recognition (HTR)

This model performs Handwritten Text Recognition in Norwegian. It was developed during the HUGIN-MUNIN project.

Model description

The model has been trained using the PyLaia library on the NorHand v1 document images.

Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio.

split	N horizontal lines
train	19,653
val	2,286
test	1,793

An external 6-gram character language model can be used to improve recognition. The language model is trained on the text from the NorHand v1 training set.

Evaluation results

The model achieves the following results:

set	Language model	CER (%)	WER (%)	N lines
test	no	7.94	24.04	1,793
test	yes	6.55	18.20	1,793

How to use

Please refer to the documentation.

Cite us!

@inproceedings{pylaia-lib,
    author = "Tarride, Solène and Schneider, Yoann and Generali, Marie and Boillet, Melodie and Abadie, Bastien and Kermorvant, Christopher",
    title = "Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library",
    booktitle = "Submitted at ICDAR2024",
    year = "2024"
}

@inproceedings{10.1007/978-3-031-06555-2_27,
    author = {Maarand, Martin and Beyer, Yngvil and K\r{a}sen, Andre and Fosseide, Knut T. and Kermorvant, Christopher},
    title = {A Comprehensive Comparison of Open-Source Libraries for Handwritten Text Recognition in Norwegian},
    year = {2022},
    isbn = {978-3-031-06554-5},
    publisher = {Springer-Verlag},
    address = {Berlin, Heidelberg},
    url = {https://doi.org/10.1007/978-3-031-06555-2_27},
    doi = {10.1007/978-3-031-06555-2_27},
    booktitle = {Document Analysis Systems: 15th IAPR International Workshop, DAS 2022, La Rochelle, France, May 22–25, 2022, Proceedings},
    pages = {399–413},
    numpages = {15},
    keywords = {Norwegian language, Open-source, Handwriting recognition},
    location = {La Rochelle, France}
}