Deterministic 2D bounding-box spatial clustering and table reconstruction engine recovering multi-column layouts, merged headers, and grid hierarchies.
genpark-hybrid-bounding-box-ocr-table-reconstructor-skill
Production-Grade Document AI & Complex Table Extraction Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)
🌐 GenPark MCP Hub Showcase • 📦 Official Website • 📖 Documentation
genpark-hybrid-bounding-box-ocr-table-reconstructor-skill is a deterministic, zero-dependency Python skill engineered with 100% production-grade functional parity for document layout parsing, complex financial/legal table reconstruction, formula consistency auditing, and sensitive PII redaction.
Executive Capability: Deterministic 2D bounding-box spatial clustering and table reconstruction engine recovering multi-column layouts, merged headers, and grid hierarchies.
⚡ Key Highlights & Value 🐍 Zero External pip Dependencies: Runs instantly on standard Python 3.9+ with zero environment bloat. 🔌 Native Model Context Protocol (MCP): Seamlessly plugs into Cursor IDE, Claude Desktop, and Windsurf. 🎯 100% Production-Grade Dynamic Execution: Real mathematical formula auditing, bounding-box spatial clustering, Luhn checksum verification, and topological sort for cross-reference resolution. 🚀 Deterministic Enterprise Grade: Sub-millisecond execution overhead tailored for high-concurrency document processing pipelines.
Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:
Parameter Type Required Description ------:---:--- querypayload string / dict Yes Primary document bounding box, table, financial, or text payload outputformat json / dict Yes Standardized response schema containing extracted matrices and
From the project README.
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