---
library_name: Doc-UFCN
license: mit
tags:
- Doc-UFCN
- PyTorch
- object-detection
- dla
- historical
metrics:
- IoU
- F1
- AP@.5
- AP@.75
- AP@[.5,.95]
pipeline_tag: image-segmentation
---


# Doc-UFCN - Generic page detection

The generic page detection model predicts single pages from document images.

## Model description

The model has been trained using the Doc-UFCN library on [Horae](https://github.com/oriflamms/HORAE/) and [READ-BAD](https://github.com/ctensmeyer/pagenet) datasets.
It has been trained on images with their largest dimension equal to 768 pixels, keeping the original aspect ratio.

## Evaluation results

The model achieves the following results:

|       | set      | IoU   | F1    | AP@[.5] | AP@[.75] | AP@[.5,.95] |
| ----- | -------- | ----: | ----: | ------: | -------: | ----------: |
| HOME  | test     | 93.92 | 95.84 | 98.98   | 98.98    | 97.61       |
| Horae | test     | 96.68 | 98.31 | 99.76   | 98.49    | 98.08       |
| Horae | test-300 | 95.66 | 97.27 | 98.87   | 98.45    | 97.38       |

## 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}
}
```