|
--- |
|
library_name: Doc-UFCN |
|
license: mit |
|
tags: |
|
- Doc-UFCN |
|
- PyTorch |
|
- object-detection |
|
- dla |
|
- historical |
|
metrics: |
|
- IoU |
|
- F1 |
|
- [email protected] |
|
- [email protected] |
|
- 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} |
|
} |
|
``` |
