image2text#

Extract text from images without needing a vision-capable AI model.

Usage#

text

python3 scripts/ocr.py <image path|URL|base64> [--lang <languages>] [--psm <mode>] [--raw]

Parameters#

• --lang: Language codes, comma-separated, default chi_sim+eng
  • chi_sim Simplified Chinese | chi_tra Traditional | eng English | jpn Japanese | kor Korean | and 30+ more
  • Combine: chi_sim+eng
• --psm: Page segmentation mode, default 6
  • 3 Fully automatic | 6 Block-level | 4 Single line | 11 Sparse text
• --raw: Output plain text only, no markers

Auto-Detects Input Type#

1. Local path: /Users/xxx/Downloads/xxx.png
2. Web URL: https://example.com/image.png — OSS temp links work too
3. Base64: Pasted image data from clipboard — just paste directly

Workflow#

1. Receive image input → auto-detect type (local path / URL / base64)
2. URL → curl downloads to temp file
3. Base64 → decode to temp file
4. Run tesseract OCR
5. Output plain text

Examples#

OCR a Chinese receipt:

bash

python3 scripts/ocr.py ~/Downloads/receipt.png --lang chi_sim

English + Chinese mixed:

bash

python3 scripts/ocr.py https://example.com/doc.jpg --lang chi_sim+eng

Plain text only (no markers):

bash

python3 scripts/ocr.py /path/to/image.png --raw

Requirements#

• tesseract must be installed: brew install tesseract
• Language packs auto-installed with tesseract
• On Mac: binary at /opt/homebrew/bin/tesseract
• Temp files auto-deleted after execution
• For best accuracy on receipts/screenshots: try --psm 3