TrOCR Traditional Chinese Baseline
Model Description
This is a TrOCR model trained from scratch on 4.1M synthetic Traditional Chinese OCR dataset for historical document recognition.
- Base Model: microsoft/trocr-base-stage1
- Training Data: 4.1M synthetic Traditional Chinese images
- Training Steps: 100k steps
- Task: Optical Character Recognition (OCR) for Traditional Chinese
Intended Use
This baseline model is designed for:
- Traditional Chinese text recognition
- Historical document digitization
- Vertical and horizontal text layout support
Training Details
Training Data
- Dataset: ZihCiLin/traditional-chinese-ocr-synthetic
- Size: 4.1 million image-text pairs
- Layout: 50% horizontal, 50% vertical
- Character Set: 13,172 Traditional Chinese characters (CNS11643)
Training Hyperparameters
- Batch size: 64 (per device)
- Learning rate: 5e-5
- Optimizer: AdamW
- LR scheduler: Cosine
- Training steps: 100k
- Warmup ratio: 0.06
Usage
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
# Load model and processor
processor = TrOCRProcessor.from_pretrained("ZihCiLin/trocr-traditional-chinese-baseline")
model = VisionEncoderDecoderModel.from_pretrained("ZihCiLin/trocr-traditional-chinese-baseline")
# Load image
image = Image.open("document.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)
Limitations
- Trained on synthetic data; performance may degrade on real historical documents with severe degradation
- Best performance on clean, well-printed text
- For historical manuscripts, use the finetuned variant: ZihCiLin/trocr-traditional-chinese-historical-finetune
Citation
If you use this model, please cite:
@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title={Decoding-Time Fusion of OCR and Large Language Models for Traditional Chinese Historical Document Recognition},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}
}
Related Resources
- Paper: Coming soon (ICPR 2026)
- Code: GitHub Repository
- Synthetic Generator: ocr-synth-generator
- Annotation System: document-ocr-annotation-system