Instructions to use kaan84/sinhala-print-trocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaan84/sinhala-print-trocr with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="kaan84/sinhala-print-trocr")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kaan84/sinhala-print-trocr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Attribution and Licensing Record
This file tracks asset-level licensing. It is not legal advice.
Release decision
The project metadata uses license: other with a pending-clarification label. The dataset is MIT-licensed, but the intermediate checkpoint has no declared license. Public release of derivative weights remains blocked.
| Asset | Role | Recorded status | Required action |
|---|---|---|---|
microsoft/trocr-base-printed |
Ultimate base | MIT via Microsoft UniLM | Retain attribution and MIT notice |
Ransaka/TrOCR-Sinhala |
Intermediate checkpoint | No declared license | Obtain explicit clarification before publishing derivative weights |
Ransaka/sinhala_synthetic_ocr-large |
Training data | MIT LICENSE; DOI 10.57967/hf/1809 |
Attribute Ransaka Ravihara and cite DOI |
avishadilhara/sinhala-ocr-lk-acts-1010 |
Evaluation source | CC BY 4.0 | Attribute authors and cite source |
| Tesseract | Baseline | Apache 2.0 | Record version and cite |
| Surya OCR | Optional baseline | Version-dependent code and model terms | Re-check terms at execution time |
Mandatory evidence to add
- Link to the checkpoint license-clarification discussion
- Author response or other permission record
- Immutable revisions for all Hub assets
- Copies or links to applicable notices at public release
Citations
TrOCR
@article{li2021trocr,
title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models},
author={Li, Minghao and Lv, Tengchao and Cui, Lei and Lu, Yijuan and Florencio, Dinei and Zhang, Cha and Li, Zhoujun and Wei, Furu},
journal={arXiv preprint arXiv:2109.10282},
year={2021}
}
Synthetic Sinhala OCR dataset
@misc{ravihara2024sinhala,
author={Ransaka Ravihara},
title={sinhala_synthetic_ocr-large},
year={2024},
publisher={Hugging Face},
doi={10.57967/hf/1809},
url={https://huggingface.co/datasets/Ransaka/sinhala_synthetic_ocr-large}
}
Real Sinhala legislative-acts benchmark
Add the authors' preferred BibTeX from arXiv:2606.29378 before publication. Do not reconstruct bibliographic details from memory when the canonical citation is available.