OCR-MCP: Professional Document Processing Suite

Complete document processing solution with 7 state-of-the-art OCR engines, intelligent preprocessing, document analysis, quality assessment, workflow automation, and professional web interface.

📋 Table of Contents

• 🎯 What is OCR-MCP?

• ✨ Complete Feature Suite

• 🚀 Quick Start

• 🛠️ Installation

• 🌐 Professional Web Interface

• 📖 Usage Examples

• 🔧 Configuration

• 🧠 AI Models & OCR Engines

• 🖼️ Image Preprocessing

• 🔍 Document Analysis

• 📊 Quality Assessment

• 🔄 Intelligent Workflows

• 🔄 Format Conversion

• 📷 Scanner Integration

• 📈 Performance & Benchmarks

• 🔍 API Reference

• 📚 Documentation

• 🤝 Contributing

• 📄 License

🎯 What is OCR-MCP?

OCR-MCP is a complete document processing suite built on FastMCP, providing enterprise-grade OCR capabilities with intelligent automation, professional web interface, and comprehensive document understanding tools.

🚀 Complete Document Processing Suite (Integrated)

OCR-MCP provides a full document processing ecosystem:

📥 Input Sources: Direct scanner control, file upload, batch processing 🖼️ Preprocessing: Deskew, enhance, crop, rotate, noise reduction 🔍 Analysis: Layout detection, table extraction, form analysis, metadata 📊 Quality: OCR validation, backend comparison, confidence scoring 🔄 Workflows: Custom pipelines, intelligent routing, batch automation 📄 Output: Multiple formats (text, HTML, PDF, JSON, searchable PDFs)

🤖 Intelligent Automation

• Auto-Backend Selection: Automatically chooses best OCR engine per document

• Quality-Gated Processing: Multiple attempts with quality thresholds

• Document Classification: Auto-detects document types (invoices, forms, etc.)

• Workflow Orchestration: Custom processing pipelines with conditional logic

• Batch Optimization: Concurrent processing with intelligent resource management

Primary OCR Engines

🚀 Mistral OCR 3 (December 2025) - State-of-the-Art Document Processing

• Performance: 74% win rate over Mistral OCR 2 on forms, scanned docs, complex tables, handwriting.

• Latency: ~0.7s average processing time (OCR-2512 SOTA API).

• Integration: Dedicated SOTA OCR payload for high-fidelity Markdown extraction.

• Capabilities: Advanced handwriting recognition, form processing, scanned document handling, complex table reconstruction

• Strengths: Superior accuracy on enterprise document types, cost-effective at $2/1K pages, HTML table reconstruction

• Repository: https://mistral.ai/products/ocr

• API: https://mistral.ai/docs (mistral-ocr-2512 model)

🔥 DeepSeek-OCR (October 2025) - Current State-of-the-Art

• Downloads: 4.7M+ on Hugging Face (most downloaded OCR model)

• Capabilities: Vision-language OCR with advanced text understanding

• Strengths: Multilingual support, complex layouts, mathematical formulas

• Repository: https://huggingface.co/deepseek-ai/DeepSeek-OCR

• Paper: https://arxiv.org/abs/2510.18234

🎯 Florence-2 (June 2024) - Microsoft's Vision Foundation Model

• Architecture: Unified vision-language model for various vision tasks

• OCR Capabilities: Excellent text extraction and layout understanding

• Strengths: Multi-task learning, fine-grained text recognition

• Repository: https://huggingface.co/microsoft/Florence-2-base

📊 DOTS.OCR (July 2025) - Document Understanding Specialist

• Focus: Document layout analysis, table recognition, formula extraction

• Strengths: Structured document parsing, multilingual support

• Repository: https://huggingface.co/rednote-hilab/dots.ocr

🚀 PP-OCRv5 (2025) - Industrial-Grade OCR

• Performance: PaddlePaddle's latest production-ready OCR system

• Strengths: High accuracy, fast inference, edge deployment

• Repository: https://huggingface.co/PaddlePaddle/PP-OCRv5

🎨 Qwen-Image-Layered (December 2025) - Advanced Image Decomposition

• Technology: Decomposes images into multiple independent RGBA layers

• OCR Integration: Isolate text, background, and structural elements for better OCR

• Capabilities: Layer-independent editing, resizing, repositioning, recoloring

• Repository: https://huggingface.co/Qwen/Qwen-Image-Layered

• Paper: https://arxiv.org/abs/2512.15603

• Use Case: Pre-process complex documents by separating text layers from backgrounds

OCR Capabilities

• Plain Text OCR: Standard text extraction from images

• Formatted Text OCR: Preserves layout and formatting structure

• Fine-Grained OCR: Extract text from specific regions with coordinate precision

• Multi-Crop OCR: Process documents with complex layouts by dividing into regions

• HTML Rendering: Generate HTML output with visual layout preservation

• Document Understanding: Table extraction, formula recognition, layout analysis

Auto-Backend Selection

OCR-MCP automatically selects the best backend based on:

• Document Type: PDF, image, scanned document, or comic

• Content Complexity: Plain text vs. structured documents

• Language Requirements: Multilingual content detection

• Performance Needs: Speed vs. accuracy trade-offs

Advanced Document Pre-processing

Qwen-Image-Layered Integration revolutionizes OCR through intelligent image decomposition:

• Layer Separation: Decompose documents into independent RGBA layers (text, background, images, graphics)

• Selective OCR: Process text layers independently for improved accuracy on complex documents

• Noise Reduction: Isolate and remove background noise, watermarks, and interfering elements

• Content Isolation: Separate handwritten notes, stamps, and annotations from main text

• Layout Preservation: Maintain document structure while enabling targeted OCR processing

• Multi-modal Enhancement: Combine with traditional OCR for hybrid processing pipelines

Community & Industry Adoption

Current OCR landscape shows rapid evolution:

• DeepSeek-OCR: Leading downloads indicate community preference

• Florence-2: Academic and research adoption

• DOTS.OCR: Document processing industry standard

• PP-OCRv5: Production deployment in enterprise applications

✨ Complete Feature Suite

🎯 Core OCR Capabilities

• 7 State-of-the-Art OCR Engines: Mistral OCR 3, DeepSeek-OCR, Florence-2, DOTS.OCR, PP-OCRv5, Qwen-Image-Layered, EasyOCR

• Intelligent Backend Selection: Auto-chooses optimal engine per document type

• Multiple Processing Modes: Text, formatted, layout preservation, fine-grained extraction

• Multi-language Support: 80+ languages across all backends

🖼️ Advanced Image Preprocessing

• Deskew: Automatic text straightening with multiple algorithms

• Enhancement: Contrast, brightness, sharpness, noise reduction

• Cropping: Auto-detect content boundaries, manual coordinates

• Rotation: Auto-detect orientation, manual angle correction

• Quality Pipeline: Complete preprocessing workflow

🔍 Document Structure Analysis

• Layout Detection: Headers, paragraphs, columns, sections

• Table Extraction: Structured data from complex tables

• Form Analysis: Checkbox, text field, signature detection

• Reading Order: Logical text flow determination

• Document Classification: Auto-detect document types

📊 Quality Assessment & Validation

• OCR Accuracy Scoring: Character, word, and sequence accuracy

• Backend Comparison: Performance analysis across engines

• Confidence Analysis: Detailed confidence metrics and thresholds

• Ground Truth Validation: Compare against known correct text

• Quality Recommendations: Automated improvement suggestions

🔄 Intelligent Workflow Automation

• Custom Pipeline Builder: Drag-and-drop workflow creation

• Quality Gates: Conditional processing based on results

• Batch Orchestration: Concurrent processing with progress tracking

• Error Recovery: Automatic retry with fallback strategies

• Resource Optimization: Intelligent load balancing

🔄 Professional Format Conversion

• PDF Processing: Extract images, create searchable PDFs

• Image Conversion: Format conversion with quality control

• Document Assembly: Combine images into PDFs

• Searchable PDFs: OCR text embedded as invisible layers

• Multi-format Export: Text, HTML, JSON, XML, Word

📷 Complete Scanner Integration

• WIA Support: Direct Windows scanner control

• Device Discovery: Auto-detect connected scanners

• Advanced Settings: DPI, color modes, paper sizes, brightness/contrast

• Batch Scanning: ADF support with page separation

• Preview Mode: Positioning and cropping verification

🌐 Professional Web Interface

The OCR-MCP web interface is accessible at:

• URL: http://localhost:8765

• Dashboard: Real-time monitoring of all OCR and scanner operations

• Scanner Control: Direct hardware acquisition with live preview

• Batch Processing: Parallel document processing with progress tracking

• Hardware Backend: Robust WIA 2.0 implementation with global singleton management for device stability.

🏗️ Architecture

AI Models & OCR Engines

OCR-MCP integrates 8 state-of-the-art AI models for comprehensive document processing:

Primary AI Models (7 Advanced Backends)

🚀 DeepSeek-OCR - Vision-language model for complex documents 🎨 Florence-2 - Microsoft's unified vision foundation model 📊 DOTS.OCR - Document table and structure specialist 🏭 PP-OCRv5 - Industrial-grade PaddlePaddle OCR 🖼️ Qwen-Image-Layered - Advanced image decomposition 🎯 GOT-OCR 2.0 - General OCR theory implementation

Legacy/Compatibility Models

📖 Tesseract OCR - Classic open-source OCR engine 🔤 EasyOCR - Ready-to-use OCR with GPU support

Model Capabilities Matrix

📖 Complete AI Models Documentation - Detailed information about all integrated AI models, performance benchmarks, and technical specifications.

Portmanteau Tool Ecosystem (6 Tools)

🎯 Document Processing (Portmanteau Tool)

document_processing(operation="...") - Consolidates OCR, analysis, and quality assessment

• "process_document": Single document OCR with backend selection

• "process_batch": Concurrent batch document processing

• "extract_regions": Fine-grained region-based OCR

• "analyze_layout": Document structure and layout detection

• "extract_table_data": Structured table data extraction

• "detect_form_fields": Form element identification

• "analyze_reading_order": Logical text flow determination

• "classify_document": Auto-document type classification

• "extract_metadata": Dates, names, numbers extraction

• "assess_quality": Comprehensive OCR quality scoring

• "compare_backends": Backend performance comparison

• "validate_accuracy": Ground truth accuracy validation

• "analyze_image_quality": Pre-OCR quality assessment

🖼️ Image Management (Portmanteau Tool)

image_management(operation="...") - Consolidates preprocessing and conversion operations

• "deskew": Straighten skewed/scanned documents

• "enhance": Improve image quality (contrast, sharpness, noise reduction)

• "rotate": Rotate images by angle or auto-detect orientation

• "crop": Remove unwanted borders or focus on content areas

• "preprocess": Complete preprocessing pipeline for OCR

• "convert_format": Convert between image formats with quality control

• "convert_pdf_to_images": Extract images from PDF documents

• "embed_ocr_text": Create searchable PDFs with embedded OCR text

📷 Scanner Operations (Portmanteau Tool)

scanner_operations(operation="...") - Consolidates all scanner hardware control

• "list_scanners": Discover and enumerate available scanners

• "scanner_properties": Get detailed scanner capabilities and settings

• "configure_scan": Set scan parameters (DPI, color mode, paper size)

• "scan_document": Perform single document scan

• "scan_batch": Batch scan multiple documents with ADF support

• "preview_scan": Low-resolution preview scan for positioning

🔄 Workflow Management (Portmanteau Tool)

workflow_management(operation="...") - Consolidates batch processing and system operations

• "process_batch_intelligent": Intelligent batch processing with quality control

• "create_processing_pipeline": Create custom processing workflows

• "execute_pipeline": Run custom pipelines on documents

• "monitor_batch_progress": Track batch processing status and metrics

• "optimize_processing": Optimize batch processing parameters

• "ocr_health_check": System health and backend status

• "list_backends": Available OCR backends and capabilities

• "manage_models": GPU memory and model lifecycle management

❓ Help & Documentation (Portmanteau Tool)

help(level="...", topic="...") - Contextual help and documentation

• "basic": Quick start guide and essential commands

• "intermediate": Detailed tool descriptions and workflows

• "advanced": Technical architecture and implementation details

• "expert": Development troubleshooting and system internals

📊 System Status (Portmanteau Tool)

status(level="...", focus="...") - System monitoring and diagnostics

• "basic": Quick system health overview

• "intermediate": Detailed backend and resource status

• "advanced": Comprehensive diagnostics with performance metrics

• Custom focus areas: "backends", "memory", "disk", "network"

WebApp Architecture

🚀 Quick Start

Prerequisites

• Python 3.11+

• GPU recommended (for GOT-OCR2.0 and other ML models)

• 8GB+ VRAM for optimal performance

Installation

MCP Configuration

Add to your claude_desktop_config.json:

WebApp Mode

OCR-MCP includes a full-featured web interface for document processing. The webapp can connect to a separately running OCR-MCP server instance.

Option 1: Run Webapp with Auto-Starting MCP Server (Recommended)

Option 2: Run MCP Server and Webapp Separately

If the automatic MCP server startup doesn't work, run them separately:

Terminal 1 - Start MCP Server:

Terminal 2 - Start Webapp:

The web interface provides:

• 📤 Drag & drop file upload - Support for PDF, images, CBZ

• 🔄 Real-time processing - Live status updates and progress

• 📷 Scanner integration - Direct scanner control via web interface

• 📊 Batch processing - Process multiple documents simultaneously

• 🎨 OCR backend selection - Choose from 5 different OCR engines

• 📋 Results visualization - Text, JSON, and HTML output formats

Access the webapp at: http://localhost:15550

🌐 Professional Web Interface

OCR-MCP features a comprehensive professional web interface designed for enterprise document processing workflows.

🎨 Interface Overview

🚀 Key Features

• 📊 Workflow-Based Processing: Step-by-step guidance through complex document processing

• 🎯 Intelligent Automation: Auto-selection of optimal tools and settings

• 📈 Real-Time Analytics: Live quality metrics, confidence scores, processing times

• 🔄 Batch Orchestration: Concurrent processing with detailed progress monitoring

• 🎨 Visual Results: Multiple output viewers (text, structured data, analysis)

• ⚙️ Advanced Configuration: Fine-grained control over all processing parameters

• 📱 Responsive Design: Works on desktop, tablet, and mobile devices

📱 Interface Sections

📤 Single Document Processing

4-Step Intelligent Workflow:

1. Upload: Drag-drop with format validation and preview

2. Preprocessing: Visual before/after with deskew, enhance, crop tools

3. OCR Processing: Backend selection with advanced options

4. Results & Analysis: Multi-format output with quality metrics

Features:

• Real-time processing status with progress bars

• Quality score display (A-F grading system)

• Confidence metrics and accuracy analysis

• Export to 6+ formats (Text, JSON, HTML, PDF, Word, XML)

📦 Intelligent Batch Processing

Smart Multi-Document Processing:

• Strategy Selection: Auto, Quality-Focused, Speed, Custom Pipeline

• Quality Gates: Configurable thresholds with automatic retries

• Progress Dashboard: Real-time status for up to hundreds of documents

• Concurrent Processing: Optimized resource utilization

• Results Aggregation: Summary statistics and error reporting

Dashboard Features:

• Individual document status tracking

• Success/failure rates and time estimates

• Quality distribution analysis

• Bulk export and reporting tools

🖼️ Image Preprocessing Studio

Professional Image Enhancement:

• Visual Editor: Before/after comparison with split-view

• Tool Palette: Deskew, enhance, crop, rotate with live preview

• Quality Analysis: Automatic assessment of improvement effectiveness

• Batch Processing: Apply pipelines to multiple images

• Parameter Control: Fine-grained adjustment of all enhancement settings

🔍 Document Analysis Lab

Advanced Structure Detection:

• Layout Analysis: Header/footer detection, column identification

• Table Extraction: Structured data from complex table layouts

• Form Detection: Checkbox, text field, signature recognition

• Reading Order: Logical text flow determination

• Type Classification: Auto-document type identification

• Metadata Extraction: Dates, names, numbers, addresses

📊 Quality Assessment Center

OCR Validation & Optimization:

• Single Assessment: Comprehensive quality scoring for individual results

• Backend Comparison: Performance analysis across all OCR engines

• Accuracy Validation: Ground truth comparison with detailed metrics

• Image Quality Check: Pre-OCR quality analysis and recommendations

• Confidence Analysis: Detailed confidence scoring and error patterns

🔄 Custom Pipeline Builder

Workflow Orchestration:

• Visual Designer: Drag-and-drop pipeline creation

• Step Library: All 20+ tools as reusable components

• Conditional Logic: Quality gates and decision branches

• Template System: Pre-built pipelines for common scenarios

• Execution Monitoring: Real-time pipeline progress and debugging

📷 Scanner Control Center

Professional Scanning:

• Device Discovery: Auto-detection of WIA-compatible scanners

• Advanced Settings: DPI, color modes, paper sizes, brightness/contrast

• Preview Mode: Positioning verification before final scan

• Batch Scanning: ADF support with automatic page separation

• Integration: Seamless workflow connection to OCR processing

🔧 Technical Architecture

Frontend Stack

• Vanilla JavaScript: No heavy frameworks, fast loading

• Modern CSS: Grid, Flexbox, CSS Variables, Animations

• Responsive Design: Mobile-first approach

• Progressive Enhancement: Works without JavaScript

• Accessibility: WCAG 2.1 AA compliance

Backend Integration

• FastAPI Server: Async processing with automatic MCP server management

• RESTful API: Clean endpoints for all functionality

• Real-time Updates: WebSocket-based progress monitoring

• File Security: Secure temporary file handling

• Error Recovery: Comprehensive error handling and user feedback

Performance Optimizations

• Lazy Loading: Components load on demand

• Background Processing: Non-blocking operations

• Smart Caching: Results caching to avoid redundant processing

• Resource Management: Intelligent memory and CPU utilization

• Progressive Rendering: Fast initial load with incremental enhancement

🎯 User Experience Highlights

Smart Defaults

• Intelligent backend selection based on document type

• Automatic preprocessing pipeline recommendations

• Quality threshold suggestions per document type

Guided Workflows

• Step-by-step processing guidance

• Contextual help and tooltips

• Progressive disclosure of advanced options

Quality Assurance

• Real-time quality metrics during processing

• Automatic suggestions for improvement

• Validation against quality thresholds

Batch Intelligence

• Optimal concurrent processing limits

• Automatic retry on failures

• Quality-based prioritization

Export Flexibility

• Multiple format support with one-click conversion

• Bulk export capabilities

• Custom export profiles

📊 Monitoring & Analytics

System Health

• Real-time backend availability status

• Resource utilization monitoring

• Performance metrics dashboard

Processing Analytics

• Success/failure rate tracking

• Average processing times by backend

• Quality score distributions

Batch Monitoring

• Individual document status

• Overall progress visualization

• Error pattern analysis

🔒 Security & Privacy

• File Security: Secure temporary file handling with automatic cleanup

• No External Calls: All processing happens locally

• Data Privacy: No document content sent to external services

• Local Processing: Complete offline capability

• Audit Trail: Processing history and error logging

💡 Usage Examples

Basic OCR Processing

Formatted OCR with HTML Output

Fine-grained Region Extraction

Batch Processing

🎨 Advanced Features

Document Layout Analysis

Multi-Backend Comparison

Image Preprocessing

🔧 Configuration Options

Environment Variables

• OCR_CACHE_DIR: Model cache directory (default: ~/.cache/ocr-mcp)

• OCR_DEVICE: Computing device (cuda, cpu, auto)

• OCR_MAX_MEMORY: Maximum GPU memory usage in GB

• OCR_DEFAULT_BACKEND: Default OCR backend (got-ocr, tesseract, etc.)

• OCR_BATCH_SIZE: Default batch processing size

Backend-Specific Settings

📊 Performance Benchmarks

Single Image Processing (GTX 3080)

Accuracy Comparison (Clean Documents)

🛠️ Development Status

• ✅ Planning: Complete master plan and architecture

• ✅ Phase 1: Core infrastructure (Completed)

• ✅ Phase 2: Multi-backend OCR support (Completed)

• ✅ Phase 3: Professional web interface (Completed)

• ✅ Phase 4: Advanced document processing (Completed)

• ✅ Phase 5: Scanner integration (Completed)

• 🟡 Phase 6: Production deployment and optimization (Alpha Release)

• 🔄 Phase 7: Beta testing and community feedback (Next)

• 🔄 Phase 8: Production release preparation (Future)

✅ Completed Features

• FastMCP 2.14.3 Integration: State-of-the-art MCP server with conversational features

• 8 AI Models: DeepSeek-OCR, Florence-2, DOTS.OCR, PP-OCRv5, Qwen-Image-Layered, GOT-OCR 2.0, EasyOCR, Tesseract

• Professional React Webapp: Complete TypeScript frontend with modern UI/UX

• Intelligent Backend Selection: Automatic model routing based on document analysis

• Document Processing Pipeline: Multi-stage OCR with quality assessment

• Advanced Image Preprocessing: Real-time enhancement with visual feedback

• Scanner Integration: Direct WIA hardware control for Windows scanners

• Batch Processing: Concurrent document processing with progress monitoring

• Quality Assessment: OCR validation with accuracy metrics and recommendations

• Format Conversion: Export to PDF, Word, JSON, HTML, and searchable PDFs

• Comprehensive Error Handling: Structured errors with recovery suggestions

• Cross-Platform Support: Windows and Linux with appropriate abstractions

• Complete Documentation: AI models guide, technical specifications, testing framework

See OCR-MCP_MASTER_PLAN.md for detailed roadmap.

📚 Documentation

📖 Complete Documentation Suite

• AI_MODELS.md - Comprehensive documentation of all 8 AI models used in OCR-MCP

  • Detailed model specifications and capabilities

  • Performance benchmarks and accuracy comparisons

  • Technical implementation details and integration guides

  • Model selection algorithms and optimization strategies

• OCR-MCP_MASTER_PLAN.md - Technical master plan and architecture

  • System design and component architecture

  • Implementation roadmap and milestones

  • Technical specifications and requirements

  • Future development plans

• tests/README.md - Testing framework documentation

  • Test organization and execution

  • Performance benchmarking procedures

  • Security testing methodologies

  • CI/CD integration guides

🛠️ Development Resources

• API Documentation: http://localhost:15550/docs (when server is running)

• Health Monitoring: http://localhost:15550/api/health

• Interactive API Explorer: Full Swagger UI with live testing

📋 Quick Reference

🤝 Integration with Existing MCP Servers

CalibreMCP Integration

OCR-MCP enhances CalibreMCP's OCR capabilities:

Document Processing Workflows

• Research Papers: Extract structured text from academic PDFs

• Receipt Processing: Automated data extraction from scanned receipts

• Book Digitization: High-quality OCR for scanned books

• Accessibility: Convert images to readable text for screen readers

📈 Roadmap

✅ Completed Milestones

• FastMCP 2.13+ Core Infrastructure

• GOT-OCR2.0 Multi-mode Integration

• Robust WIA 2.0 Hardware Integration (Canon LiDE 400 verified)

• Professional React/Next.js Web Interface

• Mistral OCR 3 (OCR-2512) SOTA Backend Implementation

• Multi-format Pipeline (PDF, CBZ, Scanned Docs)

Immediate (Next 2-4 weeks)

• Performance Benchmarking Suite

• Advanced Image Preprocessing (Deskew/Enhance)

• TWAIN Backend Support

• Multi-language Model Fine-tuning

Medium-term (2-3 months)

• Advanced Layout Intelligence (Panel analysis for Manga)

• Batch processing concurrency optimizations

• Cloud deployment (Docker/Kubernetes)

• Mobile scanning workflow integration

🤝 Contributing

Development Setup

1. Clone the repository

   git clone https://github.com/your-username/ocr-mcp.git cd ocr-mcp

2. Install Poetry (if not already installed)

   pip install poetry

3. Install dependencies

   poetry install

4. Set up development environment (recommended)

   poetry run ocr-mcp-setup-dev # This installs pre-commit hooks and sets up the development environment

5. Run tests

   poetry run pytest

6. Start developing!

   • Pre-commit hooks will automatically format and lint your code

   • Run poetry run pre-commit run --all-files to check everything

   • Use poetry run python scripts/run_webapp.py to start the webapp

Pre-commit Hooks

This project uses pre-commit hooks to maintain code quality. The following tools are automatically run on each commit:

• Ruff: Fast Python linter, formatter, and import sorter

• MyPy: Type checker

• Bandit: Security linter

• Detect-secrets: Secret detection

• Markdownlint: Markdown linter

To manually run all checks:

OCR-MCP welcomes contributions! Areas of particular interest:

• New OCR Backends: Integration of additional OCR engines

• Performance Optimization: GPU memory management, batch processing

• Specialized Models: Domain-specific OCR improvements

• Documentation: Usage examples, integration guides

• Testing: Comprehensive test coverage and benchmarks

📄 License

MIT License - see LICENSE for details.

🙏 Acknowledgments

• GOT-OCR2.0 Team (UCAS): Revolutionary OCR model that inspired this project

• FastMCP Community: Excellent framework for MCP server development

• Open Source OCR Community: Tesseract, EasyOCR, PaddleOCR, and others

OCR-MCP: Democratizing state-of-the-art document understanding for the MCP ecosystem! 🌟

See OCR-MCP_MASTER_PLAN.md for technical details and implementation roadmap.