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---
library_name: Doc-UFCN
license: mit
tags:
- Doc-UFCN
- PyTorch
- Object detection
metrics:
- IoU
- F1
- [email protected]
- [email protected]
- AP@[.5,.95]
---
# Hugin-Munin line detection
The Hugin-Munin line detection model predicts text lines from Hugin-Munin document images. This model was developed during the [HUGIN-MUNIN project](https://hugin-munin-project.github.io/).
## Model description
The model has been trained using the Doc-UFCN library on Hugin-Munin document images.
It has been trained on images with their largest dimension equal to 768 pixels, keeping the original aspect ratio.
The model predicts two classes: vertical and horizontal text lines.
## Evaluation results
The model achieves the following results:
| set | class | IoU | F1 | AP@[.5] | AP@[.75] | AP@[.5,.95] |
| ----- | ---------- | ----- | ----- | ------- | -------- | ----------- |
| train | vertical | 88.29 | 89.67 | 71.37 | 33.26 | 36.32 |
| | horizontal | 69.81 | 81.35 | 91.73 | 36.62 | 45.67 |
| val | vertical | 73.01 | 75.13 | 46.02 | 4.99 | 15.58 |
| | horizontal | 61.65 | 75.69 | 87.98 | 11.18 | 31.55 |
| test | vertical | 78.62 | 80.03 | 59.93 | 15.90 | 24.11 |
| | horizontal | 63.59 | 76.49 | 95.93 | 24.18 | 41.45 |
## How to use
Please refer to the Doc-UFCN library page (https://pypi.org/project/doc-ufcn/) to use this model.
# Cite us!
```bibtex
@inproceedings{boillet2020,
author = {Boillet, Mélodie and Kermorvant, Christopher and Paquet, Thierry},
title = {{Multiple Document Datasets Pre-training Improves Text Line Detection With
Deep Neural Networks}},
booktitle = {2020 25th International Conference on Pattern Recognition (ICPR)},
year = {2021},
month = Jan,
pages = {2134-2141},
doi = {10.1109/ICPR48806.2021.9412447}
}
```
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