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OCR Comprehensive Comparison — API vs Open Source vs Fine-tuning, What Should You Use?
Accuracy, cost, security, difficulty, and maintenance of 3 approaches in one chart
3 Approaches
1. API → send image to Google/Azure, get text back
2. Self-hosted → run EasyOCR/PaddleOCR on your server
3. Fine-tuned → additionally train TrOCR/manga-ocr on your data
5-Axis Comparison
| Axis | API | Self-hosted | Fine-tuned |
|---|---|---|---|
| Accuracy | ★★★★★ | ★★★☆☆ | ★★★★☆ |
| Cost | Per-page | Server only | Server + training |
| Security | ❌ External | ✅ Local | ✅ Local |
| Difficulty | ★☆☆☆☆ | ★★☆☆☆ | ★★★☆☆ |
| Maintenance | Provider manages | You update | You retrain |
Decision Flowchart
Confidential? → YES → Self-host/Fine-tune. GPU? → YES → Sarashina2.2. NO → PaddleOCR.
Confidential? → NO → API possible. Low volume? → Google Vision. High volume? → Self-host cheaper.
Key Concepts
1
API (★☆☆☆☆) — just get API key, use immediately. No model management
2
Self-hosting (★★☆☆☆) — pip install + 3 lines. Free. Secure. Server management needed
3
Fine-tuning (★★★☆☆) — hundreds of data + Trainer.train(). Beats API on specialized domains
4
Confidential → local (self-host/fine-tune), non-confidential → API OK
5
In practice, mix by document type — API for non-confidential, local for confidential
Use Cases
OCR architecture design — choose optimal combination based on confidentiality, cost, accuracy
Cost optimization — calculate breakpoint between API (low volume) and self-hosting (high volume)
Security compliance — meet local execution requirements for confidential document processing