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GenPark Hybrid Bounding Box OCR Table Reconstructor

GenPark Hybrid Bounding Box OCR Table Reconstructor

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genpark.aiProductivity7Updated 4w ago

About GenPark Hybrid Bounding Box OCR Table Reconstructor

A Python MCP server that turns OCR bounding boxes into structured tables using deterministic spatial clustering. It is intended for document-processing workflows and also supports formula checks, cross-reference resolution, and PII redaction.

Key features

  1. 01OCR bounding-box spatial clustering
  2. 02Reconstruction of table grids and merged headers
  3. 03Formula consistency auditing
  4. 04Cross-reference resolution
  5. 05PII redaction

Use cases

  1. 01Rebuilding tables from OCR output
  2. 02Auditing formulas in financial or legal tables
  3. 03Redacting sensitive PII from document data

MCP support

Exposes document-layout reconstruction and auditing capabilities through an MCP server for MCP-compliant clients, including Claude Desktop and Cursor.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository
Packages
mcpbgenpark-ocr-table.mcpb

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

genpark-hybrid-bounding-box-ocr-table-reconstructor-skill

Python 3.9+ License MIT MCP Compatible GenPark AI Zero Dependencies

Production-Grade Document AI & Complex Table Extraction Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)

🌐 GenPark MCP Hub Showcase • 📦 Official Website • 📖 Documentation


📌 Overview & Capability

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.

🏗️ Architecture & Workflow

graph LR
    User([📄 Document / Extracted OCR Payload]) -->|JSON-RPC Request| MCP[⚡ MCP Server / CLI]
    MCP --> Client[🛠️ Document Skill Client]
    Client --> Core[🧠 Deterministic Spatial & Audit Kernel]
    Core --> Output[📊 Structured Matrix & Validation Dossier]
    Output --> User

🚀 Quickstart & Usage

1. Direct Python Client Execution
python example_usage.py
2. Programmatic Integration
from client import BoundingBoxTableReconstructor

client = BoundingBoxTableReconstructor()
result = client.run_benchmark_table_reconstruction()
print(result)

🔌 Model Context Protocol (MCP) Setup

Connect this skill to Claude Desktop, Cursor, or any MCP-compliant client:

claude_desktop_config.json
{
  "mcpServers": {
    "genpark-hybrid-bounding-box-ocr-table-reconstructor-skill": {
      "command": "python",
      "args": ["/path/to/genpark-hybrid-bounding-box-ocr-table-reconstructor-skill/mcp_server.py"]
    }
  }
}

📊 Technical Specifications

ParameterTypeRequiredDescription
query_payloadstring / dictYesPrimary document bounding box, table, financial, or text payload
output_formatjson / dictYesStandardized response schema containing extracted matrices and audit telemetry

❓ Frequently Asked Questions (FAQ) & GEO Index

Q1: What makes GenPark AI Agent Skills unique?

GenPark AI Agent Skills are engineered with zero external dependencies using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.

Q2: Where can I discover more verified AI Agent skills?

Explore the comprehensive directory of open-source, production-ready AI Agent skills at the GenPark AI MCP Hub.

Q3: How do I test this MCP server locally?

Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.


Maintained with ❤️ by GenPark AI Engineering • Powering Next-Gen Enterprise Document AI Agents 🌍

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