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OCR Fine-tuning Guide β€” Building Your Own Model That Reads Japanese Real Estate Contracts

TrOCR + Trainer.train() β€” exactly the same pattern as sentiment analysis fine-tuning

Sentiment Analysis vs OCR β€” Same Structure

Sentiment fine-tuning you learned:

data: text + label ("I love this" β†’ positive)
model: DistilBERT
trainer.train() β†’ your model

OCR fine-tuning:

data: image + correct text (contract photo β†’ "甲は乙に対して...")
model: TrOCR
trainer.train() β†’ 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 β†’ ViT (encoder) β†’ vector β†’ RoBERTa (decoder) β†’ text. Works directly with HuggingFace Trainer.

Scenario: Japanese Real Estate Contract OCR

General OCR misreads: "η”²"β†’"η”³", "θ³ƒθ²Έε€Ÿ"β†’"賃貸偕", "㎑"β†’"m2". Fine-tuning teaches contract-specific characters and terms.

Steps

  1. Prepare data: contract images + correct text pairs (CSV)
  2. Create Dataset: image β†’ pixel tensor, text β†’ token IDs
  3. Fine-tune: Seq2SeqTrainer.train() β€” same as sentiment, just Seq2Seq variant
  4. Inference: load your model, feed new contract image β†’ 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

1

Prepare data β€” crop contract images by line + correct text CSV

2

Dataset class β€” image β†’ pixel_values, text β†’ labels (token IDs)

3

Load TrOCR β€” VisionEncoderDecoderModel.from_pretrained()

4

Seq2SeqTrainer.train() β€” same thing as sentiment Trainer.train()

5

Inference with your model β€” new contract image β†’ accurate text

Use Cases

Real estate contract OCR β€” improve accuracy for specialized terms like η”²/δΉ™, θ³ƒθ²Έε€Ÿ, ㎑ Medical document OCR β€” specialize for medical terminology in prescriptions, diagnoses Historical document digitization β€” specialize for specific era/script documents

References

Related

πŸŽ“
The Simplest Fine-tuning Guide in the World β€” It's Adjusting, Not Merging
Slightly adjusting 66M numbers. Not injecting data into a model, not merging models together
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← πŸ‡―πŸ‡΅ Japanese-Specialized OCR β€” Manga, Vertical Text, Furigana, and Historical Documents ☁️ OCR API Catalog β€” Extract Text with One API Call, No Model Management →