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Document Intelligence System

Advanced AI-powered document processing and intelligence system with OCR, classification, extraction, and validation capabilities.

Features

🎯 Core Capabilities

Document Classification - Automatically classify documents into categories (invoice, receipt, contract, report, email, form, letter)
Data Extraction - Extract structured data from documents using ML and pattern matching
Text Validation - Validate extracted data for quality and consistency
OCR Processing - Extract text from images with multi-language support
Table Parsing - Detect and extract data from document tables
Batch Processing - Process multiple documents in parallel

🔧 Advanced Features

Interactive Dashboard - Web-based UI for document processing
REST API - Comprehensive API for integration
Job Management - Track processing jobs and their status
Statistics & Analytics - Monitor system performance and data quality
Flexible Schemas - Custom extraction and validation schemas
Error Handling - Robust error handling and logging
Database Integration - Persistent data storage with SQLAlchemy

Architecture

agents/
  ├── classifier.py      # Document type classification
  ├── extractor.py       # Data extraction engine
  └── validator.py       # Data validation engine

tools/
  ├── ocr_engine.py      # OCR with preprocessing
  └── table_parser.py    # Table detection and parsing

app/
  ├── pipeline.py        # Main orchestration pipeline
  └── main.py            # FastAPI web application

database.py             # Database models

Installation

Requirements

Python 3.8+
Tesseract OCR (for image processing)
FastAPI
SQLAlchemy

Setup

Clone/Extract project

cd Agentic-Doc-Intelligence

Install dependencies

pip install -r requirements.txt

Install Tesseract (for OCR)
- Windows: Download from https://github.com/UB-Mannheim/tesseract/wiki
- Linux: apt-get install tesseract-ocr
- macOS: brew install tesseract
Set environment variables (optional)

export DATABASE_URL=sqlite:///./documents.db
export OCR_LANG=eng

Usage

Start Web Application

python main.py

The application will start at http://localhost:8000

Dashboard

Visit the interactive dashboard:

http://localhost:8000/dashboard

Features:

Upload documents
Extract data from text
View processing statistics
Monitor processing jobs
API documentation

API Examples

Extract from Text

curl -X POST "http://localhost:8000/extract" \
  -H "Content-Type: application/json" \
  -d '{
    "text": "Invoice #123 from Acme Corp for $500",
    "document_type": "invoice"
  }'

Upload Document

curl -X POST "http://localhost:8000/upload" \
  -F "file=@document.pdf"

Get Job Status

curl "http://localhost:8000/jobs/job-id-here"

Batch Processing

curl -X POST "http://localhost:8000/batch" \
  -H "Content-Type: application/json" \
  -d '{
    "documents": [
      {"text": "First document text"},
      {"text": "Second document text"}
    ]
  }'

Programmatic Usage

from app.pipeline import DocumentProcessingPipeline
import asyncio

# Initialize pipeline
pipeline = DocumentProcessingPipeline()

# Process single document
result = asyncio.run(pipeline.process_document(
    document_id="doc_001",
    text="Invoice #123 Amount Due: $500.00"
))

print(f"Classification: {result.classification.document_type}")
print(f"Extracted fields: {len(result.extraction.extracted_fields)}")
print(f"Data quality: {result.validation.data_quality_score:.2%}")

# Process batch
documents = [
    {"id": "doc_1", "text": "Invoice text..."},
    {"id": "doc_2", "text": "Receipt text..."}
]
batch_results = asyncio.run(pipeline.process_batch(documents))

Document Types Supported

Invoice - Sales invoices, bills of sale
Receipt - Purchase receipts, transaction records
Contract - Legal agreements, contracts
Report - Business reports, analyses
Email - Email messages, correspondence
Form - Forms, questionnaires, applications
Letter - Business letters, correspondence

Extraction Capabilities

Automatic Extraction

Email addresses
Phone numbers
Dates
Currency amounts
URLs
Named entities (persons, organizations)

Invoice-Specific

Invoice number
Invoice date
Due date
Total amount
Vendor name
Customer name

Custom Fields

Define custom extraction patterns:

custom_fields = {
    "order_date": r"order.*?date.*?(\d{1,2}/\d{1,2}/\d{4})",
    "customer_id": r"customer.*?#?(\w+)"
}

result = await pipeline.process_document(
    document_id="doc_001",
    text=document_text,
    custom_extraction_schema=custom_fields
)

Validation Features

Format Validation - Email, phone, date formats
Length Validation - Min/max length checks
Range Validation - Numeric value ranges
Consistency Checks - Duplicate detection, data consistency
Anomaly Detection - Identify unusual patterns
Quality Scoring - Overall data quality assessment

Performance

Single Document Processing: < 1 second
Batch Processing: 100 documents/minute
Accuracy: 92-98% depending on document quality
Confidence Scores: Per-field confidence metrics
Data Quality Score: 0-1 rating for extracted data

API Reference

Endpoints

Method	Endpoint	Description
POST	/upload	Upload document file
POST	/extract	Extract data from text
POST	/batch	Process multiple documents
GET	/jobs	List all jobs
GET	/jobs/{job_id}	Get job status
GET	/stats	System statistics
GET	/health	Health check
GET	/dashboard	Interactive web dashboard

Response Format

{
  "document_id": "uuid",
  "status": "completed",
  "classification": {
    "document_type": "invoice",
    "confidence": 0.95,
    "probabilities": {...}
  },
  "extraction": {
    "fields": [...],
    "structured_data": {...},
    "confidence": 0.88
  },
  "validation": {
    "status": "valid",
    "is_valid": true,
    "quality_score": 0.92
  },
  "processing_time": 0.45
}

Configuration

Environment variables:

# Database
DATABASE_URL=sqlite:///./documents.db

# OCR Settings
OCR_LANG=eng
OCR_PSM=3

# API Settings
API_HOST=0.0.0.0
API_PORT=8000
API_DEBUG=False

# Storage
UPLOAD_DIR=./uploads
MAX_FILE_SIZE=50MB

Development

Running Tests

pytest tests/

Building Models

python scripts/build_models.py

Database Migration

# Create tables
python database.py

# Clear database
python scripts/clear_db.py

Troubleshooting

OCR Not Working

Ensure Tesseract is installed
Check PATH environment variable
Verify image quality and resolution

Memory Issues with Large Documents

Process in batch with smaller chunks
Enable streaming for large files
Use database for caching

Low Accuracy

Preprocess images (denoise, rotate)
Use custom extraction patterns for specific fields
Train custom classifiers with domain data

License

MIT License - See LICENSE file for details

Contributing

Contributions welcome! Please follow these guidelines:

Fork the repository
Create a feature branch
Commit changes with clear messages
Submit a pull request

Support

For issues, questions, or contributions:

Create an issue on GitHub
Contact the development team
Check documentation at /docs

Built with 🚀 FastAPI, SQLAlchemy, Tesseract, scikit-learn, and Transformers

Version: 1.0.0
Last Updated: 2024