Trocr Traditional Chinese Baseline

--- language: - zh tags: - trocr - ocr - traditional-chinese - vision - image-to-text datasets: - ZihCiLin/traditional-chinese-ocr-synthetic metrics: - cer library_name: transformers --- # 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## Model Description This is a TrOCR model trained from scratch on 4.1M synthetic Traditional Chinese OCR dataset for historical document recognition.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for Traditional Chinese ## Intended Use This baseline model is designed for: - Traditional Chinese text recognition - Historical document digitization - Vertical and horizontal text layout support ## Training Details ### Training Data - **Dataset**: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](https://huggingface.co/datasets/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 ```python 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print(generated_text) ``` ## Limitations - Trained on synthetic data; performance may degrade on real historical documents with severe degradation - Best performance on clean, well-printed text - For historical manuscripts, use the finetuned variant: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](https://huggingface.co/ZihCiLin/trocr-traditional-chinese-historical-finetune) ## Citation If you use this model, please cite: ```bibtex @inproceedings{lin2026decoding, title={Decoding-Time Fusion of OCR and Large...

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