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 (https://pypi.org/project/doc-ufcn/) to use this model.

Cite us!

bibtex

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    title = {{Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods}},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

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}
}

johnlockejrr/doc-ufcn-samaritan

Doc-UFCN - Samaritan manuscripts line detection

Model description

How to use?

Cite us!

johnlockejrr/
doc-ufcn-samaritan