ocr_processor.py

import cv2
import numpy as np
import pytesseract
from PIL import Image
import re

class PrescriptionOCR:
    def __init__(self):
        self.medication_keywords = [
            'tablet', 'capsule', 'mg', 'ml', 'injection', 'dose',
            'twice', 'thrice', 'daily', 'weekly', 'monthly'
        ]
    
    def preprocess_image(self, image):
        """Enhanced image preprocessing for medical prescriptions"""
        try:
            # Convert to numpy array
            img_array = np.array(image)
            
            # Convert to grayscale
            if len(img_array.shape) == 3:
                gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
            else:
                gray = img_array
            
            # Noise removal
            denoised = cv2.medianBlur(gray, 3)
            
            # Thresholding
            _, thresh = cv2.threshold(denoised, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
            
            # Morphological operations
            kernel = np.ones((2, 2), np.uint8)
            processed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
            
            return processed
            
        except Exception as e:
            print(f"Image preprocessing error: {e}")
            return np.array(image)
    
    def extract_medication_info(self, text):
        """Extract medication information from OCR text"""
        medications = []
        lines = text.split('\n')
        
        for line in lines:
            line_clean = line.strip()
            if any(keyword in line_clean.lower() for keyword in self.medication_keywords):
                # Extract dosage information
                dosage_pattern = r'(\d+\s*(?:mg|ml|tablet|cap)s?)'
                dosages = re.findall(dosage_pattern, line_clean, re.IGNORECASE)
                
                # Extract frequency
                freq_pattern = r'(?:once|twice|thrice|\d+\s*times)\s*(?:daily|a day|per day)'
                frequency = re.findall(freq_pattern, line_clean, re.IGNORECASE)
                
                medication_info = {
                    'text': line_clean,
                    'dosages': dosages,
                    'frequency': frequency[0] if frequency else 'Unknown',
                    'confidence': 'High' if dosages else 'Medium'
                }
                medications.append(medication_info)
        
        return medications
    
    def process_prescription(self, image):
        """Main method to process prescription and extract information"""
        try:
            # Preprocess image
            processed_img = self.preprocess_image(image)
            
            # OCR with medical-specific configuration
            custom_config = r'--oem 3 --psm 6 -l eng'
            extracted_text = pytesseract.image_to_string(processed_img, config=custom_config)
            
            # Extract medication information
            medications = self.extract_medication_info(extracted_text)
            
            # Calculate confidence score
            confidence = self.calculate_confidence(extracted_text, medications)
            
            return {
                'raw_text': extracted_text,
                'medications': medications,
                'confidence_score': confidence,
                'medication_count': len(medications)
            }
            
        except Exception as e:
            return {
                'raw_text': '',
                'medications': [],
                'confidence_score': 0,
                'error': str(e)
            }
    
    def calculate_confidence(self, text, medications):
        """Calculate confidence score for OCR extraction"""
        if not text.strip():
            return 0
        
        # Base score based on text length and medication detection
        base_score = min(100, len(text) / 10)
        
        # Bonus for medication detection
        medication_bonus = len(medications) * 15
        
        # Penalty for likely errors
        error_penalty = 0
        if len(text) < 20:
            error_penalty += 20
        if len(re.findall(r'[^\w\s.,]', text)) > len(text) * 0.3:
            error_penalty += 15
        
        final_score = base_score + medication_bonus - error_penalty
        return max(0, min(100, final_score))