ddddocr CAPTCHA Recognition
A CAPTCHA recognition server built by ymeng98 that integrates the ddddocr Python library for automated solving of various CAPTCHA types through a TypeScript MCP interface. The implementation provides tools for OCR text recognition, object detection in image-based CAPTCHAs, slider puzzle matching, and health monitoring, using a hybrid architecture where TypeScript handles MCP communication while spawning Python processes to execute ddddocr operations on base64-encoded images or file paths. It supports multiple CAPTCHA formats including text-based verification codes, click-to-select object challenges, and sliding puzzle verification, making it useful for web scraping automation, testing workflows, and accessibility tools that need to programmatically solve visual verification challenges.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-01.
Scan details: Circle-IR · 2026-09-01 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- media
- Source
- PulseMCP
- Repository
- github.com/ymeng98/ddddocr-captcha-mcp
- Author type
- human
- Last scanned
- 2026-09-01
- Updated
- 2026-09-01
Use via MCP
Resolve ddddocr CAPTCHA Recognition from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp --scope user for --scope project to commit it to .mcp.json.