OCR Fine-tuning Guide — Building Your Own Model That Reads Japanese Real Estate Contracts
TrOCR + Trainer.train() — exactly the same pattern as sentiment analysis fine-tuning
Sentiment Analysis vs OCR — Same Structure
Sentiment fine-tuning you learned:
data: text + label ("I love this" → positive)
model: DistilBERT
trainer.train() → your model
OCR fine-tuning:
data: image + correct text (contract photo → "甲は乙に対して...")
model: TrOCR
trainer.train() → your model
Input changed from text to image. That's it. Trainer.train() is identical.
Why TrOCR Is Easiest
TrOCR is a standard VisionEncoderDecoderModel: Image → ViT (encoder) → vector → RoBERTa (decoder) → text. Works directly with HuggingFace Trainer.
Scenario: Japanese Real Estate Contract OCR
General OCR misreads: "甲"→"申", "賃貸借"→"賃貸偕", "㎡"→"m2". Fine-tuning teaches contract-specific characters and terms.
Steps
- Prepare data: contract images + correct text pairs (CSV)
- Create Dataset: image → pixel tensor, text → token IDs
- Fine-tune:
Seq2SeqTrainer.train()— same as sentiment, just Seq2Seq variant - Inference: load your model, feed new contract image → recognized text
Easier Alternative: Fine-tune manga-ocr
manga-ocr already understands Japanese. Fine-tuning from manga-ocr to contracts needs fewer data (hundreds vs tens of thousands from English TrOCR).
Key Concepts
Prepare data — crop contract images by line + correct text CSV
Dataset class — image → pixel_values, text → labels (token IDs)
Load TrOCR — VisionEncoderDecoderModel.from_pretrained()
Seq2SeqTrainer.train() — same thing as sentiment Trainer.train()
Inference with your model — new contract image → accurate text