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

class ChuukeseOCRProcessor:
    def __init__(self):
        # Configure Tesseract for multi-language support
        self.tesseract_config = {
            'chuukese_optimized': '--oem 3 --psm 6 -c tessedit_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzáéíóúāēīōū0123456789.,!?;:()-"\' ',
            'multilingual': '--oem 3 --psm 6',
            'preserve_structure': '--oem 3 --psm 1'
        }
        
        # Chuukese character mappings for OCR corrections
        self.ocr_corrections = {
            # Common OCR mistakes for accented characters
            'a´': 'á', 'a`': 'à', 'a¯': 'ā',
            'e´': 'é', 'e`': 'è', 'e¯': 'ē',
            'i´': 'í', 'i`': 'ì', 'i¯': 'ī',
            'o´': 'ó', 'o`': 'ò', 'o¯': 'ō',
            'u´': 'ú', 'u`': 'ù', 'u¯': 'ū',
            
            # Common character confusions
            '0': 'o', '1': 'l', '5': 's',
            'rn': 'm', 'cl': 'd', 'ck': 'ch'
        }
    
    def preprocess_image(self, image_path):
        """Preprocess image for better OCR accuracy"""
        # Load image
        image = cv2.imread(image_path)
        
        # Convert to grayscale
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        
        # Noise removal
        denoised = cv2.medianBlur(gray, 3)
        
        # Contrast enhancement
        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
        enhanced = clahe.apply(denoised)
        
        # Binarization
        _, binary = cv2.threshold(enhanced, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        
        return binary

class ChuukeseOCRPostProcessor:
    def __init__(self, dictionary_path=None):
        self.dictionary = {}
        if dictionary_path:
            self.load_chuukese_dictionary(dictionary_path)
        
        # Common OCR error patterns for Chuukese
        self.error_patterns = {
            # Accent corrections
            r'a[\'\`\´]': 'á',
            r'e[\'\`\´]': 'é',
            r'i[\'\`\´]': 'í',
            r'o[\'\`\´]': 'ó',
            r'u[\'\`\´]': 'ú',
            
            # Common character substitutions
            r'\b0(?=[aeiou])': 'o',  # 0 at start of word -> o
            r'(?<=[aeiou])0\b': 'o',  # 0 at end after vowel -> o
            r'\brn(?=[aeiou])': 'm',   # rn -> m
        }
    
    def correct_ocr_errors(self, text):
        """Apply OCR error corrections specific to Chuukese"""
        corrected = text
        
        # Apply pattern-based corrections
        for pattern, replacement in self.error_patterns.items():
            corrected = re.sub(pattern, replacement, corrected)
        
        return corrected

# Initialize processor
processor = BatchOCRProcessor("output/ocr_results")

# Process single document
result = processor.process_document("scanned_chuukese_dictionary.jpg")

# Access extracted text
extracted_text = result['extracted_text']
dictionary_entries = result['document_structure']['dictionary_entries']

# Process all images in a directory
batch_results = processor.process_batch(
    "scanned_documents/",
    file_patterns=['*.jpg', '*.png']
)

print(f"Processed {batch_results['successfully_processed']} documents")