File size: 1,608 Bytes
8e0b955 58700dc 8e0b955 58700dc 65c3ab3 58700dc 65c3ab3 8e0b955 ae14f1d 65c3ab3 ae14f1d 2a973ee ae14f1d 2a973ee ae14f1d 21f246a ae14f1d |
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 |
---
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}
}
``` |