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Add model, configuration files and description (#1)

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- Add model, configuration files and description (46954747b3693a724ca4054210de985f013b1a5d)


Co-authored-by: Mélodie Boillet <[email protected]>

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  1. README.md +75 -0
  2. model.pth +3 -0
  3. parameters.yml +11 -0
README.md CHANGED
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  ---
 
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  license: mit
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ metrics:
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+ - IoU
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+ - F1
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+ - AP@[.5,.95]
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  ---
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+
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+
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+ # Generic historical line detection
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+
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+ The generic historical line detection model predicts text lines from document images.
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+
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+ ## Model description
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+
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+ The model has been trained using the Doc-UFCN library on 10 historical document datasets including these public datasets: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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+
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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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+
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+ ## Evaluation results
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+
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+ The model achieves the following results on the test sets:
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+
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+ | | 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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+
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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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+
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+ ## How to use
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+
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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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+
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+ # Cite us!
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+
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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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+
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+ ```bibtex
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+ @inproceedings{boillet2020,
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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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+ ```
model.pth ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:b5d0659a29638446f573bec8928f3380f8643f4bee5c9ea7b8e64ae813dcc6f5
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+ size 49198561
parameters.yml ADDED
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+ ---
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+ version: 0.0.1
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+ parameters:
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+ mean: [194, 185, 160]
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+ std: [49, 49, 47]
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+ min_cc: 50
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+ classes:
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+ - background
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+ - text_line
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+ input_size: 768
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+