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OCR Open Source Catalog — Every Way to Extract Text from Images
From Tesseract to EasyOCR, PaddleOCR, Surya, TrOCR, Qwen2-VL — difficulty, language, speed comparison
OCR = image → model(image) → text
Same pattern as everything else.
Easiest: EasyOCR (3 lines)
import easyocr
reader = easyocr.Reader(['en'])
result = reader.readtext('doc.png')
Tool Comparison
| Tool | Type | Size | GPU | CJK | Difficulty |
|---|---|---|---|---|---|
| EasyOCR | CNN+RNN | ~100MB | No | Yes | ★☆☆☆☆ |
| Tesseract | LSTM | ~15MB | No | Yes | ★★☆☆☆ |
| PaddleOCR | CNN+RNN | 10-150MB | No | Best | ★★☆☆☆ |
| Surya | Transformer | ~300MB | Rec | Yes | ★★☆☆☆ |
| Qwen2-VL | LLM | 2B+ | Yes | Yes | ★★★★☆ |
Key Concepts
1
EasyOCR (★☆☆☆☆) — pip install + 3 lines. 80 languages. Easiest
2
PaddleOCR (★★☆☆☆) — CJK strongest. Lightweight model 10MB
3
Surya (★★☆☆☆) — Latest Transformer. 90 languages. Benchmark best
4
Tesseract (★★☆☆☆) — Lightest at 15MB. 100 languages. No GPU
5
Qwen2-VL (★★★★☆) — Multimodal LLM. "Read it" one-shot OCR. GPU required
Use Cases
Document digitization — extract text from scanned docs/images
Scene text recognition — extract text from signs, menus, business cards
RAG pipeline — OCR docs to text → store in vector DB → LLM QA