Create README.md

README.md

@@ -0,0 +1,89 @@
+---
+license: mit
+language:
+- la
+- fr
+- esp
+datasets:
+- CATMuS/medieval
+tags:
+- trocr
+- image-to-text
+widget:
+- src: >-
+    https://huggingface.co/medieval-data/trocr-medieval-print/resolve/main/images/print-1.png
+  example_title: Print 1
+- src: >-
+    https://huggingface.co/medieval-data/trocr-medieval-print/resolve/main/images/print-2.png
+  example_title: Print 2
+- src: >-
+    https://huggingface.co/medieval-data/trocr-medieval-print/resolve/main/images/print-3.png
+  example_title: Print 3
+metrics:
+- cer: 0.05
+---
+
+![logo](logo-print.png)
+
+# About
+
+CER: 0.05
+
+This is a TrOCR model for medieval Print. The base model was [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten). The model was then finetuned to Caroline: [medieval-data/trocr-medieval-latin-caroline](https://huggingface.co/medieval-data/trocr-medieval-latin-caroline). From a saved checkpoint, the model was further finetuned to Print.
+
+The dataset used for training was [CATMuS](https://huggingface.co/datasets/CATMuS/medieval).
+
+The model has not been formally tested. Preliminary examination indicates that further finetuning is needed.
+
+Finetuning was done with finetune.py found in this repository.
+
+# Usage
+
+```python
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+import requests
+
+# load image from the IAM database
+url = 'https://huggingface.co/medieval-data/trocr-medieval-print/resolve/main/images/print-1.png'
+image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+
+processor = TrOCRProcessor.from_pretrained('medieval-data/trocr-medieval-print')
+model = VisionEncoderDecoderModel.from_pretrained('medieval-data/trocr-medieval-print')
+pixel_values = processor(images=image, return_tensors="pt").pixel_values
+
+generated_ids = model.generate(pixel_values)
+generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+# BibTeX entry and citation info
+
+## TrOCR Paper
+
+```tex
+@misc{li2021trocr,
+      title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models}, 
+      author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei},
+      year={2021},
+      eprint={2109.10282},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+## CATMuS Paper
+
+```tex
+@unpublished{clerice:hal-04453952,
+  TITLE = {{CATMuS Medieval: A multilingual large-scale cross-century dataset in Latin script for handwritten text recognition and beyond}},
+  AUTHOR = {Cl{\'e}rice, Thibault and Pinche, Ariane and Vlachou-Efstathiou, Malamatenia and Chagu{\'e}, Alix and Camps, Jean-Baptiste and Gille-Levenson, Matthias and Brisville-Fertin, Olivier and Fischer, Franz and Gervers, Michaels and Boutreux, Agn{\`e}s and Manton, Avery and Gabay, Simon and O'Connor, Patricia and Haverals, Wouter and Kestemont, Mike and Vandyck, Caroline and Kiessling, Benjamin},
+  URL = {https://inria.hal.science/hal-04453952},
+  NOTE = {working paper or preprint},
+  YEAR = {2024},
+  MONTH = Feb,
+  KEYWORDS = {Historical sources ; medieval manuscripts ; Latin scripts ; benchmarking dataset ; multilingual ; handwritten text recognition},
+  PDF = {https://inria.hal.science/hal-04453952/file/ICDAR24___CATMUS_Medieval-1.pdf},
+  HAL_ID = {hal-04453952},
+  HAL_VERSION = {v1},
+}
+```