Doc-UFCN PyTorch Object detection

Hugin-Munin line detection

The Hugin-Munin line detection model predicts text lines from Hugin-Munin document images. This model was developed during the HUGIN-MUNIN project.

Model description

The model has been trained using the Doc-UFCN library on Hugin-Munin document images. It has been trained on images with their largest dimension equal to 768 pixels, keeping the original aspect ratio. The model predicts two classes: vertical and horizontal text lines.

Evaluation results

The model achieves the following results:

set class IoU F1 AP@[.5] AP@[.75] AP@[.5,.95]
train vertical 88.29 89.67 71.37 33.26 36.32
horizontal 69.81 81.35 91.73 36.62 45.67
val vertical 73.01 75.13 46.02 4.99 15.58
horizontal 61.65 75.69 87.98 11.18 31.55
test vertical 78.62 80.03 59.93 15.90 24.11
horizontal 63.59 76.49 95.93 24.18 41.45

How to use

Please refer to the Doc-UFCN library page (https://pypi.org/project/doc-ufcn/) to use this model.

Cite us!

@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}
}