File size: 1,903 Bytes
7978f54 fc5248f 7978f54 fc5248f 469ff59 fc5248f 590629f c9931e8 8098992 469ff59 7978f54 fc5248f 0f8ad5f fc5248f 29a117e fc5248f 29a117e 6eb4072 fc5248f 6eb4072 fc5248f 6eb4072 fc5248f 29a117e fc5248f 66c2871 fc5248f 6eb4072 c9931e8 fc5248f c9931e8 fc5248f 6eb4072 8098992 c9931e8 8098992 6eb4072 8098992 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
---
library_name: PyLaia
license: mit
tags:
- PyLaia
- PyTorch
- atr
- htr
- ocr
- historical
- handwritten
metrics:
- CER
- WER
language:
- la
- fr
datasets:
- Teklia/Alcar
pipeline_tag: image-to-text
---
# PyLaia - HOME-Alcar
This model performs Handwritten Text Recognition in Latin on medieval documents.
## Model description
The model was trained using the PyLaia library on two medieval datasets:
* [Himanis](https://demo.arkindex.org/browse/5000e248-a624-4df1-8679-1b34679817ef?top_level=true&folder=true) (French);
* [HOME-Alcar](https://demo.arkindex.org/browse/46b9b1f4-baeb-4342-a501-e2f15472a276?top_level=true&folder=true) (Latin).
Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio.
An external 6-gram character language model can be used to improve recognition. The language model is trained on the text from the HOME-Alcar training set.
## Evaluation results
On HOME-Alcar text lines, the model achieves the following results:
| set | Language model | CER (%) | WER (%) | lines |
|:------|:---------------| ----------:| -------:|----------:|
| test | no | 8.35 | 26.15 | 6,932 |
| test | yes | 7.85 | 23.20 | 6,932 |
## How to use?
Please refer to the [PyLaia documentation](https://atr.pages.teklia.com/pylaia/usage/prediction/) to use this model.
## Cite us!
```bibtex
@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}
}
``` |