Usage

python

from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image

# Load model and processor
processor = TrOCRProcessor.from_pretrained("ZihCiLin/trocr-traditional-chinese-historical-finetune")
model = VisionEncoderDecoderModel.from_pretrained("ZihCiLin/trocr-traditional-chinese-historical-finetune")

# Load historical document image
image = Image.open("historical_manuscript.jpg").convert("RGB")

# Generate text
pixel_values = processor(image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

print(generated_text)

With Decoding-Time Fusion (Recommended)

For best results, use with LLM-guided decoding:

python

# See: https://github.com/Jason9339/traditional-chinese-historical-document-ocr-llm-fusion
# Fusion with Breeze-7B improves CER by 10% on historical documents

Limitations

Optimized for Lo Chia-Luen collection style (20th century Chinese manuscripts)
Performance may vary on other historical periods or writing styles
Requires reasonable image quality (severe degradation may still cause errors)

Citation

If you use this model, please cite:

bibtex

@inproceedings{lin2026decoding,
  title={Decoding-Time Fusion of OCR and Large Language Models for Traditional Chinese Historical Document Recognition},
  author={Lin, Zih-Ci and Liao, Wen-Hung},
  booktitle={International Conference on Pattern Recognition (ICPR)},
  year={2026}
}

Acknowledgments

This work is based on the Lo Chia-Luen Manuscripts from the Special Collection Center of NCCU Libraries.

Related Resources

Paper: Coming soon (ICPR 2026)
Code: GitHub Repository
Baseline Model: trocr-traditional-chinese-baseline
Annotation System: document-ocr-annotation-system