Doc-UFCN
Collection
This Doc-UFCN collection contains models designed to run various DLA tasks like the text line detection or page segmentation.
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4 items
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Updated
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2
The generic historical line detection model predicts text lines from document images.
The model has been trained using the Doc-UFCN library on 10 historical document datasets including these public datasets:
It has been trained on images with their largest dimension equal to 768 pixels, keeping the original aspect ratio.
The model achieves the following results on the test sets:
dataset | IoU | F1 | AP@[.5] | AP@[.75] | AP@[.5,.95] |
---|---|---|---|---|---|
Bozen | 60.15 | 75.10 | 97.14 | 3.79 | 27.50 |
cBAD2017 (READ) Complex | 46.79 | 60.35 | 56.01 | 3.40 | 16.26 |
cBAD2017 (READ) Simple | 53.97 | 68.43 | 57.26 | 8.45 | 19.39 |
cBAD2019 | 50.77 | 64.52 | 35.46 | 2.88 | 11.51 |
DIVA-HisDB | 41.54 | 57.88 | 63.15 | 0.00 | 11.69 |
Horae | 48.93 | 63.95 | 57.45 | 5.20 | 15.55 |
ScribbleLens | 76.61 | 86.72 | 98.02 | 71.87 | 58.32 |
The model has been trained to reduce mergers in predictions (see the paper for more details on training). Therefore, despite slightly low evaluation values, the model correctly detects lines on a wide variety of historical and modern manuscript documents.
Please refer to the Doc-UFCN library page to use this model.
@inproceedings{boillet2022,
author = {Boillet, Mélodie and Kermorvant, Christopher and Paquet, Thierry},
title = {{Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods}},
booktitle = {{International Journal on Document Analysis and Recognition (IJDAR)}},
year = {2022},
month = Mar,
pages = {1433-2825},
doi = {10.1007/s10032-022-00395-7}
}
@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}
}