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@@ -25,15 +25,15 @@ This model performs Handwritten Text Recognition in Norwegian. It was developed
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  ## Model description
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- The model was trained using the PyLaia library on the [NorHand v2 dataset](https://zenodo.org/records/10555698).
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  Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio.
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- | split | N lines | N horizontal lines |
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- | ----- | ------: | -----------------: |
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- | train | 146,693 | 145,061 |
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- | val | 15,119 | 14,980 |
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- | test | 1,830 | 1,793 |
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  An external 6-gram character language model can be used to improve recognition. The language model is trained on the text from the NorHand v2 training set.
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@@ -41,22 +41,26 @@ An external 6-gram character language model can be used to improve recognition.
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  The model achieves the following results:
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- | set | Language model | CER (%) | WER (%) | N lines |
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  |:------|:---------------| ----------:| -------:|----------:|
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  | test | no | 3.82 | 12.00 | 1,573 |
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  | test | yes | 3.13 | 9.29 | 1,573 |
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  ## How to use?
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- Please refer to the [documentation](https://atr.pages.teklia.com/pylaia/).
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  ## Cite us!
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  ```bibtex
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- @inproceedings{pylaia-lib,
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- author = "Tarride, Solène and Schneider, Yoann and Generali, Marie and Boillet, Melodie and Abadie, Bastien and Kermorvant, Christopher",
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- title = "Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library",
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- booktitle = "Submitted at ICDAR2024",
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- year = "2024"
 
 
 
 
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  }
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  ```
 
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  ## Model description
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+ The model was trained using the PyLaia library on the [NorHand v2](https://zenodo.org/records/10555698) dataset.
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  Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio.
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+ | set | lines | horizontal lines |
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+ | :---- | ------: | ---------------: |
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+ | train | 146,693 | 145,061 |
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+ | val | 15,119 | 14,980 |
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+ | test | 1,830 | 1,793 |
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  An external 6-gram character language model can be used to improve recognition. The language model is trained on the text from the NorHand v2 training set.
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  The model achieves the following results:
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+ | set | Language model | CER (%) | WER (%) | lines |
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  |:------|:---------------| ----------:| -------:|----------:|
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  | test | no | 3.82 | 12.00 | 1,573 |
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  | test | yes | 3.13 | 9.29 | 1,573 |
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  ## How to use?
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+ Please refer to the [PyLaia documentation](https://atr.pages.teklia.com/pylaia/usage/prediction/) to use this model.
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  ## Cite us!
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  ```bibtex
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+ @inproceedings{pylaia2024,
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+ author = {Tarride, Solène and Schneider, Yoann and Generali-Lince, Marie and Boillet, Mélodie and Abadie, Bastien and Kermorvant, Christopher},
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+ title = {{Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library}},
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+ booktitle = {Document Analysis and Recognition - ICDAR 2024},
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+ year = {2024},
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+ publisher = {Springer Nature Switzerland},
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+ address = {Cham},
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+ pages = {387--404},
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+ isbn = {978-3-031-70549-6}
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  }
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  ```