Skill: jessy-huang/paddle-ocr-vl (version 1.0.0)

--- name: paddle-ocr-vl description: GPU-accelerated document parsing and OCR via PaddleOCR-VL. Detects layout, recognizes Chinese/English text, tables, charts, and seals in images. Use when the user asks to OCR an image, extract text from a document, parse a screenshot, or recognize text in photos. Supports vertical classical Chinese text, modern newspaper layouts, and mixed-content documents. version: 1.0.0 metadata: clawdbot: requires: env: - (none) bins: - docker - python3 - nvidia-smi emoji: "🔍" homepage: https://github.com/user/paddle-ocr-vl-skill skills: - mcp-server - ocr - document-parsing --- # PaddleOCR-VL Skill GPU-accelerated document OCR using the PaddleOCR-VL model running inside an ephemeral Docker container. Auto-detects NVIDIA GPU architecture (Blackwell SM120 vs. standard) and selects the correct official image. ## When to Use - User provides an image path and asks to "read", "OCR", or "extract text" from it - User wants to parse a document screenshot, newspaper page, or classical text - User asks about the content of an image file ## Architecture This skill includes an **MCP server** (`server.py`) that exposes three tools: | Tool | Purpose | |---|---| | `run_ocr` | OCR any image — provide an absolute path | | `check_environment` | Verify Docker, GPU drivers, and image are ready | | `run_demo` | Run OCR on bundled demo images to test the setup | ## Setup ### 1. Install the MCP Server Add to `~/.config/Claude/claude_desktop_config.json` (Claude Desktop) or `~/.claude/settings.json` (Claude Code): ```json { "mcpServers": { "paddle-ocr-vl": { "command": "python3", "args": ["<INSTALL_DIR>/server.py"] } } } ``` ### 2. Pull the Docker Image (one-time) ```bash # Blackwell GPU (RTX 50xx, B100/B200 — compute capability >= 12.0): docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-vl:latest-nvidia-gpu-sm120 # Other NVIDIA GPU: docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/paddleocr-vl:latest-nvidia-gpu ``` ### 3. Verify Call `check_environment` to verify everything is set up, then `run_demo` to test on the bundled sample images. ## Bundled Demo Images | File | Content | |---|---| | `demo/newspaper.png` | People's Daily article about China-Eritrea relations | | `demo/classical_text.png` | Records of the Three Kingdoms, vertical classical Chinese | ## Requirements - Docker with `nvidia-container-toolkit` - NVIDIA GPU with drivers installed - Python 3.10+ with `mcp` SDK (`pip install mcp`) ## Security & Privacy - Images are processed inside an ephemeral Docker container (`--rm` flag) - The container has no network access beyond `--network host` (needed for GPU) - No data leaves the host machine - The container is destroyed immediately after each OCR run ## External Endpoints None. All processing is local. ## Official References - PaddleOCR-VL docs: https://www.paddleocr.ai/latest/version3.x/pipeline_usage/PaddleOCR-VL.html - Blackwell-specific: https://www.paddleocr.ai/latest/version3.x/pipeline_usage/PaddleOCR-VL-NVIDIA-Blackwell.html


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)