doc-ufcn-generic-historical-line

Doc-UFCN - Generic historical line detection

The generic historical line detection model predicts text lines from document images.

Model description

The model has been trained using the Doc-UFCN library on 10 historical document datasets including these public datasets:

• Bozen;
• cBAD2017 (READ);
• cBAD2019;
• DIVA-HisDB;
• Horae;
• ScribbleLens.

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 on the test sets:

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.

How to use?

Please refer to the Doc-UFCN library page to use this model.

Cite us!

@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    title = {{Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods}},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}
}