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doc-ufcn-generic-page
Teklia/doc-ufcn-generic-page
doc-ufcn-generic-page is a image segmentation model from Teklia. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for Doc-UFCN. The card lists the license as mit.
The generic page detection model predicts single pages from document images.
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Public
Updated Sep 10, 2024
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49.2 MB
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At a glance
- Task
- Image Segmentation
- Library
- Doc-UFCN
- License
- mit
- Access
- Public
- Created
- Nov 2, 2022
- Updated
- Sep 10, 2024
- SHA
- 0bdb94c3
Spaces
Model card
From the Hugging Face model README
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 and READ-BAD 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:
| dataset | 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 to use this model.
Cite us!
@inproceedings{doc_ufcn2021,
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}
}
- Task
- Image Segmentation
- Library
- Doc-UFCN
- License
- mit
- Created
- Nov 2, 2022
- Updated
- Sep 10, 2024