metadata

library_name: PyLaia
license: mit
tags:
  - PyLaia
  - PyTorch
  - Handwritten text recognition
metrics:
  - CER
  - WER
language:
  - lat

HOME-Alcar and Himanis handwritten text recognition

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

Model description

The model has been trained using the PyLaia library on the NorHand document images. Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio.

Evaluation results

The model achieves the following results:

Himanis:

set	CER (%)	WER (%)	support
train	5.31	17.47	18503
val	10.37	27.63	2367
test	9.87	28.27	2241

Alcar:

set	CER (%)	WER (%)	support
train	4.74	17.29	59969
val	7.82	23.67	7905
test	8.34	24.57	6932

How to use

Please refer to the PyLaia library page (https://pypi.org/project/pylaia/) to use this model.

Cite us!

@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 Latin},
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 = {Latin language, Open-source, Handwriting recognition},
location = {La Rochelle, France}
}