Document OCR Processing

Overview

Specialized OCR processing for documents containing Chuukese text, with enhanced accuracy for accented characters, traditional formatting patterns, and multilingual content. Designed to handle the unique challenges of digitizing historical and contemporary Chuukese documents.

Capabilities

• Chuukese-Aware OCR: Enhanced recognition of accented characters (á, é, í, ó, ú, ā, ē, Ä«, ō, Å«)
• Traditional Format Recognition: Handle traditional document layouts and formatting
• Multilingual Processing: Process documents with both Chuukese and English text
• Quality Enhancement: Post-processing to improve OCR accuracy
• Batch Processing: Efficiently process multiple documents
• Format Preservation: Maintain original document structure and layout

Core Components

1. OCR Engine Setup

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

2. Post-Processing for Chuukese Text

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

Usage Examples

Process Single Document

# 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']

Batch Process Directory

# 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")

Best Practices

Image Preprocessing

1. Quality assessment: Check image quality before processing
2. Resolution optimization: Ensure minimum 300 DPI for OCR
3. Noise reduction: Apply appropriate filtering for cleaner text
4. Orientation correction: Detect and correct page rotation

OCR Accuracy

1. Language-specific tuning: Optimize for Chuukese character set
2. Confidence thresholds: Filter low-confidence results
3. Multiple engine comparison: Use different OCR engines for comparison
4. Human validation: Sample-based quality checking

Dependencies

• pytesseract: OCR engine interface
• opencv-python: Image preprocessing
• Pillow: Image handling and manipulation
• numpy: Numerical operations for image processing