| name | ocr |
| description | Local OCR via LightOnOCR-2-1B + llama.cpp on macOS (Apple Silicon). Use this skill whenever the user wants to extract text from images, convert screenshots to Markdown, OCR documents, or batch-process images to text. Triggers on: OCR, 图片转文字, 截图转Markdown, extract text from image, 识别图片文字, 批量OCR. The tool is ~6s per image on M4 Metal GPU, outputs clean Markdown. Already installed at ~/proj/ocr-models/ and ~/.local/bin/ocr. |
Local OCR — LightOnOCR-2-1B via llama.cpp (macOS Metal GPU)
Zero-dependency image-to-Markdown OCR. Already deployed on this machine.
Quick reference
ocr <image>
ocr <image> -o out.md
ocr <dir>
Environment
| Component | Path |
|---|
| llama.cpp | /opt/homebrew/bin/llama-mtmd-cli (brew, arm64) |
| Model GGUF | ~/proj/ocr-models/LightOnOCR-2-1B-Q4_K_M.gguf (378MB) |
| Vision encoder | ~/proj/ocr-models/LightOnOCR-2-1B-mmproj-f16.gguf (781MB) |
| Wrapper script | ~/.local/bin/ocr |
| Conda env | ocr (python=3.12, huggingface_hub for model downloads) |
Model: wangjazz/LightOnOCR-2-1B-gguf — 1B params, Q4_K_M quantization, 228 tok/s on M4 Max, excellent Chinese + English, tables/receipts/math support.
Usage patterns
When the user gives you image paths
Just run ocr <path> and read the Markdown output. For multiple images, run them in parallel:
ocr img1.jpg -o img1.md &
ocr img2.jpg -o img2.md &
wait
When the user gives you a directory of images
ocr /path/to/dir
When you need to adjust quality
OCR_TEMP=0.0 ocr image.jpg
OCR_MAX_TOKENS=4000 ocr image.jpg
When the user asks about OCR setup
They already have llama.cpp via brew, the model in ~/proj/ocr-models/, and the ocr script in PATH. If anything is missing, see references/setup.md for the full installation guide.
Limitations
- ~6 seconds per image on this M4 Mac (Metal GPU, 16GB RAM)
- Image quality matters: blurry screenshots → degraded output; clean screenshots → near-perfect
- Memory: ~2GB VRAM during inference (Q4_K_M quantized)
- Multi-page PDF: not directly supported — extract pages to images first, then batch OCR
