File size: 1,566 Bytes
72feb57 aa5dfb9 72feb57 aa5dfb9 16eded6 aa5dfb9 888a92e 16eded6 72feb57 aa5dfb9 16eded6 aa5dfb9 6a41675 aa5dfb9 888a92e |
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 |
---
library_name: PyLaia
license: mit
tags:
- PyLaia
- PyTorch
- atr
- htr
- ocr
- historical
- handwritten
metrics:
- CER
- WER
language:
- fr
datasets:
- Teklia/POPP
pipeline_tag: image-to-text
---
# PyLaia - POPP
This model performs Handwritten Text Recognition on French census documents.
## Model description
The model was trained using the PyLaia library on the [POPP generic](https://github.com/Shulk97/POPP-datasets/).
For training, text-lines were resized with a fixed height of 128 pixels, keeping the original aspect ratio.
| split | N lines |
| ----- | ------: |
| train | 3,835 |
| val | 480 |
| test | 479 |
An external 6-gram character language model can be used to improve recognition. The language model is trained on the text from the POPP training set.
## Evaluation results
The model achieves the following results:
| set | Language model | CER (%) | WER (%) | N lines |
|:------|:---------------|:----------:|:-------:|----------:|
| test | no | 16.49 | 36.26 | 479 |
| test | yes | 16.09 | 34.52 | 479 |
## How to use
Please refer to the [documentation](https://atr.pages.teklia.com/pylaia/).
## Cite us
```bibtex
@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"
}
``` |