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Backend, model, and task are configured by the user via `/ocr` settings — the AI doesn't need to manage them.\n\n---\n\n## Backends\n\nSwitch anytime with `/ocr` (no args).\n\n| | Backend | Best for | Setup |\n|---|---|---|---|\n| ☁️ | **MinerU** (default) | PDFs, general docs | None |\n| ☁️ | **MinerU Pro** | Large PDFs, vlm accuracy | API token |\n| 🦙 | Ollama | Math formulas → LaTeX | GPU + 2.2GB model |\n| 🔤 | Tesseract | Plain text (~30MB) | `brew install tesseract` |\n| 📐 | Pix2Text | Math + text, GPU/CPU | `pip install pix2text` |\n\n> 💡 Unsure which backend to pick? See the [benchmark](pi-ocr-benchmark.md) with real test results and the ground truth for comparison.\n\n---\n\n## MinerU (default)\n\nFree cloud API. Images are wrapped as PDF so language-aware OCR applies.\n\n**Limits:** ≤10MB, ≤20 pages/request. PDFs >20 pages auto-split via pypdfium2.\n\n---\n\n## MinerU Pro (vlm model)\n\nHigher accuracy via token-based precision API. **≤200MB, ≤200 pages** — no splitting needed.\n\nGet a free token at [mineru.net/apiManage](https://mineru.net/apiManage), then set it in `/ocr` settings. 1000 pages/day high-priority.\n\n---\n\n## Ollama\n\nLocal GPU OCR via [glm-ocr](https://ollama.com) — state-of-the-art formula recognition (94.6 OmniDocBench). Outputs LaTeX.\n\n```bash\n# macOS\nbrew install ollama && ollama pull glm-ocr\nbrew install poppler   # multi-page PDFs\n\n# Linux\ncurl -fsSL https://ollama.com/install.sh | sh\nollama pull glm-ocr\nsudo apt install poppler-utils\n```\n\n---\n\n## Tesseract\n\nClassic OCR engine. Ultra-lightweight (~30MB). **No formula support** — use Ollama or Pix2Text for math.\n\n```bash\nbrew install tesseract              # macOS\nsudo apt install tesseract-ocr      # Linux\n```\n\nSupports Chinese: `brew install tesseract-lang` (auto-installed on macOS).\n\n---\n\n## Pix2Text\n\nMathpix alternative — handles text + formulas on GPU (CUDA/MPS) or CPU. Auto-detects best device.\n\n```bash\npip install pix2text\n```\n\nFirst run downloads ONNX models (~50MB).\n\n---\n\n## Settings\n\nOpen with `/ocr` (no args).\n\n| Setting | Description |\n|---|---|\n| OCR Backend | Switch between MinerU, Ollama, Pix2Text, Tesseract |\n| MinerU: Split PDF >20 pages | Auto-split large PDFs into free-tier chunks |\n| MinerU Pro Token | API token from mineru.net/apiManage |\n| Ollama Model | Vision model (glm-ocr, minicpm-v, etc.) |\n| Clear OCR temp files | Remove cached OCR output from /tmp |\n\n---\n\n## Output Behavior\n\nResults ≤2000 chars are returned inline in the tool response. Longer results are written to a temp file (`/tmp/pi-ocr-*.md`); the tool response includes the file path for the AI to read.\n\n---\n\n## Commands\n\n| Command | |\n|---|---|\n| `/ocr` | Open settings (backend, model, split toggle, clear cache) |\n| `/ocr <file>` | OCR a file |\n| `/ocr <file> formula` | Math LaTeX output (Ollama backend) |\n\n## Troubleshooting\n\n**MinerU 429** → Wait a minute or switch backend.\n\n**MinerU Pro 401** → Regenerate token at mineru.net/apiManage.\n\n**\"Is Ollama running?\"** → `ollama serve`\n\n**\"pdftoppm not found\"** → `brew install poppler` / `sudo apt install poppler-utils`\n\n**\"python3 not found\" (Pix2Text)** → `pip install pix2text`\n\n**\"tesseract not found\"** → `brew install tesseract` / `sudo apt install tesseract-ocr`\n\n---\n\n## License\n\nMIT\n","readmeFilename":"README.md"}