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README.md

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----
-license: cc0-1.0
----
+---
+library_name: PyLaia
+license: mit
+tags:
+- PyLaia
+- PyTorch
+- atr
+- htr
+- ocr
+- historical
+- handwritten
+metrics:
+- CER
+- WER
+language:
+- fr
+base_model: Teklia/pylaia-norhand-v3
+datasets:
+- Teklia/PELLET-Casimir-Marius-line
+pipeline_tag: image-to-text
+---
+
+# PyLaia - PELLET Casimir Marius - Line level
+
+This model performs Handwritten Text Recognition in French. Trained following [Teklia's tutorial](https://doc.arkindex.org/tutorial/). 
+
+## Model description
+
+The model has been trained, for 100 epochs, using the PyLaia library on the [PELLET Casimir Marius - Line level](Teklia/PELLET-Casimir-Marius-line) dataset.
+
+Training images were resized with a fixed height of 128 pixels, keeping the original aspect ratio.
+
+| set   | lines |
+| :---- | ----: |
+| train |   842 |
+| val   |   125 |
+| test  |   122 |
+
+## Evaluation results
+
+The model achieves the following results:
+
+| set   | CER (%) | WER (%) | text_line |
+| :---- | ------: | ------: | --------: |
+| train |   24.17 |   58.12 |       842 |
+| val   |    22.9 |   58.75 |       125 |
+| test  |   18.78 |      50 |       122 |
+
+## How to use?
+
+Please refer to the [PyLaia documentation](https://atr.pages.teklia.com/pylaia/usage/prediction/) to use this model.
+
+## Cite us!
+
+
+```bibtex
+@inproceedings{pylaia2024,
+    author = {Tarride, Solène and Schneider, Yoann and Generali-Lince, Marie and Boillet, Mélodie and Abadie, Bastien and Kermorvant, Christopher},
+    title = {{Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source Library}},
+    booktitle = {Document Analysis and Recognition - ICDAR 2024},
+    year = {2024},
+    publisher = {Springer Nature Switzerland},
+    address = {Cham},
+    pages = {387--404},
+    isbn = {978-3-031-70549-6}
+}
+```
+
+<style>
+    table {
+        width: 50%;
+    }
+</style>

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weights.ckpt

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