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--- |
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library_name: Doc-UFCN |
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license: mit |
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tags: |
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- Doc-UFCN |
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- PyTorch |
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- object-detection |
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- dla |
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- historical |
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- handwritten |
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metrics: |
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- IoU |
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- F1 |
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- AP@.5 |
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- AP@.75 |
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- AP@[.5,.95] |
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pipeline_tag: image-segmentation |
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--- |
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# Doc-UFCN - Generic historical line detection |
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The generic historical line detection model predicts text lines from document images. |
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## Model description |
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The model has been trained using the Doc-UFCN library on 10 historical document datasets including these public datasets: |
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* [Bozen](https://zenodo.org/record/218236); |
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* [cBAD2017 (READ)](https://zenodo.org/record/1491441); |
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* [cBAD2019](https://zenodo.org/record/2567398); |
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* [DIVA-HisDB](https://diuf.unifr.ch/main/hisdoc/diva-hisdb.html); |
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* [Horae](https://github.com/oriflamms/HORAE/); |
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* [ScribbleLens](https://www.openslr.org/84/). |
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It has been trained on images with their largest dimension equal to 768 pixels, keeping the original aspect ratio. |
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## Evaluation results |
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The model achieves the following results on the test sets: |
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| dataset | IoU | F1 | AP@[.5] | AP@[.75] | AP@[.5,.95] | |
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| :---------------------- | ----: | ----: | ------: | -------: | ----------: | |
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| Bozen | 60.15 | 75.10 | 97.14 | 3.79 | 27.50 | |
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| cBAD2017 (READ) Complex | 46.79 | 60.35 | 56.01 | 3.40 | 16.26 | |
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| cBAD2017 (READ) Simple | 53.97 | 68.43 | 57.26 | 8.45 | 19.39 | |
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| cBAD2019 | 50.77 | 64.52 | 35.46 | 2.88 | 11.51 | |
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| DIVA-HisDB | 41.54 | 57.88 | 63.15 | 0.00 | 11.69 | |
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| Horae | 48.93 | 63.95 | 57.45 | 5.20 | 15.55 | |
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| ScribbleLens | 76.61 | 86.72 | 98.02 | 71.87 | 58.32 | |
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The model has been trained to reduce mergers in predictions (see the [paper](https://link.springer.com/article/10.1007/s10032-022-00395-7) 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. |
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## How to use? |
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Please refer to the [Doc-UFCN library page](https://pypi.org/project/doc-ufcn/) to use this model. |
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## Cite us! |
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```bibtex |
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@inproceedings{boillet2022, |
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author = {Boillet, Mélodie and Kermorvant, Christopher and Paquet, Thierry}, |
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title = {{Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods}}, |
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booktitle = {{International Journal on Document Analysis and Recognition (IJDAR)}}, |
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year = {2022}, |
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month = Mar, |
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pages = {1433-2825}, |
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doi = {10.1007/s10032-022-00395-7} |
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} |
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``` |
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```bibtex |
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@inproceedings{doc_ufcn2021, |
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author = {Boillet, Mélodie and Kermorvant, Christopher and Paquet, Thierry}, |
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title = {{Multiple Document Datasets Pre-training Improves Text Line Detection With |
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Deep Neural Networks}}, |
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booktitle = {2020 25th International Conference on Pattern Recognition (ICPR)}, |
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year = {2021}, |
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month = Jan, |
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pages = {2134-2141}, |
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doi = {10.1109/ICPR48806.2021.9412447} |
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} |
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``` |
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