Intelligent Document Processing for Financial Services

# Confer's Self-Healing PDF Parser Implementation

import os
import numpy as np
import cv2
import pytesseract
from pdf2image import convert_from_path
from typing import Dict, List, Any, Tuple
from dataclasses import dataclass

@dataclass
class DocumentField:
    """Represents a field extracted from a document."""
    name: str
    value: str
    confidence: float
    bounding_box: Tuple[int, int, int, int]
    page_number: int

class SelfHealingPDFParser:
    """Advanced PDF parser with self-healing capabilities for mortgage documents."""
    
    def __init__(
        self,
        model_path: str = "models/mortgage_doc_classifier.pkl",
        template_dir: str = "templates/",
        confidence_threshold: float = 0.75,
        enable_self_healing: bool = True
    ):
        self.model_path = model_path
        self.template_dir = template_dir
        self.confidence_threshold = confidence_threshold
        self.enable_self_healing = enable_self_healing
        self.load_models()
        
    def load_models(self):
        """Load document classification and field extraction models."""
        # Load document classifier model
        # Load field extraction models
        # Initialize OCR engine with optimized parameters
        pass
        
    def process_document(self, file_path: str) -> Dict[str, Any]:
        """Process a document and extract structured data."""
        # Convert PDF to images
        images = self._pdf_to_images(file_path)
        
        # Classify document type
        doc_type, doc_confidence = self._classify_document(images[0])
        
        # Select appropriate extraction strategy based on document type
        if doc_type == "loan_estimate":
            return self._process_loan_estimate(images)
        elif doc_type == "closing_disclosure":
            return self._process_closing_disclosure(images)
        else:
            return self._process_generic_document(images, doc_type)
    
    def _pdf_to_images(self, pdf_path: str) -> List[np.ndarray]:
        """Convert PDF to list of images."""
        pages = convert_from_path(pdf_path, 300)
        return [np.array(page) for page in pages]
    
    def _classify_document(self, image: np.ndarray) -> Tuple[str, float]:
        """Classify document type from first page image."""
        # Preprocess image
        # Extract features
        # Apply document classification model
        # Return document type and confidence score
        return "loan_estimate", 0.98  # Placeholder
    
    def _process_loan_estimate(self, images: List[np.ndarray]) -> Dict[str, Any]:
        """Extract data from Loan Estimate document."""
        result = {
            "document_type": "loan_estimate",
            "fields": {},
            "sections": {}
        }
        
        # Process each section of the LE
        result["sections"]["loan_terms"] = self._extract_loan_terms(images[0])
        result["sections"]["projected_payments"] = self._extract_projected_payments(images[0])
        result["sections"]["costs_at_closing"] = self._extract_costs_at_closing(images[0])
        
        # Process page 2 for closing cost details
        if len(images) > 1:
            result["sections"]["closing_cost_details"] = self._extract_closing_cost_details(images[1])
        
        # Apply self-healing if enabled and needed
        if self.enable_self_healing:
            result = self._apply_self_healing(result, "loan_estimate")
        
        # Validate extracted data
        result["validation"] = self._validate_loan_estimate(result)
        
        return result
    
    def _extract_loan_terms(self, image: np.ndarray) -> Dict[str, Any]:
        """Extract loan terms section from LE."""
        # Implementation details for extracting loan terms
        pass
    
    def _apply_self_healing(self, result: Dict[str, Any], doc_type: str) -> Dict[str, Any]:
        """Apply self-healing algorithms to fix extraction errors."""
        # Check for missing required fields
        # Apply business rules to detect inconsistencies
        # Use contextual information to infer missing values
        # Update confidence scores
        return result
    
    def _validate_loan_estimate(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Validate extracted LE data against business rules."""
        # Implement validation logic
        # Check for required fields
        # Verify calculations
        # Return validation results
        pass