Create arabic_connector.py

arabic_connector.py

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+"""
+Arabic OCR Text Correction Module
+
+This module provides comprehensive post-processing and correction for Arabic OCR output
+using dictionary-based fuzzy matching, context-aware selection, and linguistic knowledge.
+
+Author: AI Assistant
+License: MIT
+"""
+
+import os
+import json
+import re
+import pickle
+from typing import List, Dict, Tuple, Optional, Set
+from collections import defaultdict, Counter
+from pathlib import Path
+
+import requests
+from rapidfuzz import fuzz, process
+import pyarabic.araby as araby
+from camel_tools.utils.normalize import normalize_unicode, normalize_alef_maksura_ar, normalize_alef_ar, normalize_teh_marbuta_ar
+
+
+class ArabicTextCorrector:
+    """
+    Professional Arabic text correction system with dictionary-based fuzzy matching,
+    context-aware selection, and confidence scoring.
+    """
+    
+    def __init__(self, cache_dir: str = "./arabic_resources"):
+        """
+        Initialize the Arabic text corrector.
+        
+        Args:
+            cache_dir: Directory to cache downloaded resources
+        """
+        self.cache_dir = Path(cache_dir)
+        self.cache_dir.mkdir(exist_ok=True)
+        
+        # Core data structures
+        self.dictionary: Set[str] = set()
+        self.word_frequencies: Dict[str, int] = {}
+        self.bigrams: Dict[Tuple[str, str], int] = defaultdict(int)
+        self.trigrams: Dict[Tuple[str, str, str], int] = defaultdict(int)
+        
+        # Arabic letter similarity map for OCR error patterns
+        self.letter_similarity = self._build_letter_similarity_map()
+        
+        # Load resources
+        self._load_or_download_resources()
+        
+    def _build_letter_similarity_map(self) -> Dict[str, List[str]]:
+        """
+        Build a map of commonly confused Arabic letters in OCR.
+        
+        Returns:
+            Dictionary mapping each letter to similar-looking letters
+        """
+        return {
+            'ب': ['ت', 'ث', 'ن', 'ي'],
+            'ت': ['ب', 'ث', 'ن'],
+            'ث': ['ب', 'ت', 'ن'],
+            'ج': ['ح', 'خ'],
+            'ح': ['ج', 'خ'],
+            'خ': ['ج', 'ح'],
+            'د': ['ذ'],
+            'ذ': ['د'],
+            'ر': ['ز'],
+            'ز': ['ر'],
+            'س': ['ش'],
+            'ش': ['س'],
+            'ص': ['ض'],
+            'ض': ['ص'],
+            'ط': ['ظ'],
+            'ظ': ['ط'],
+            'ع': ['غ'],
+            'غ': ['ع'],
+            'ف': ['ق'],
+            'ق': ['ف'],
+            'ك': ['گ'],
+            'ل': ['لا'],
+            'ن': ['ب', 'ت', 'ث', 'ي'],
+            'ه': ['ة'],
+            'ة': ['ه'],
+            'و': ['ؤ'],
+            'ي': ['ئ', 'ى', 'ب', 'ت', 'ن'],
+            'ى': ['ي', 'ئ'],
+            'ا': ['أ', 'إ', 'آ'],
+            'أ': ['ا', 'إ', 'آ'],
+            'إ': ['ا', 'أ', 'آ'],
+            'آ': ['ا', 'أ', 'إ'],
+        }
+    
+    def _load_or_download_resources(self):
+        """Load or download Arabic language resources."""
+        dict_file = self.cache_dir / "arabic_dictionary.pkl"
+        freq_file = self.cache_dir / "word_frequencies.pkl"
+        ngram_file = self.cache_dir / "ngrams.pkl"
+        
+        if dict_file.exists() and freq_file.exists() and ngram_file.exists():
+            print("📚 Loading cached Arabic resources...")
+            try:
+                with open(dict_file, 'rb') as f:
+                    self.dictionary = pickle.load(f)
+                with open(freq_file, 'rb') as f:
+                    self.word_frequencies = pickle.load(f)
+                with open(ngram_file, 'rb') as f:
+                    ngram_data = pickle.load(f)
+                    self.bigrams = ngram_data['bigrams']
+                    self.trigrams = ngram_data['trigrams']
+                print(f"✅ Loaded {len(self.dictionary)} Arabic words")
+                return
+            except Exception as e:
+                print(f"⚠️ Error loading cache: {e}. Downloading fresh...")
+        
+        print("📥 Downloading Arabic language resources...")
+        self._download_arabic_wordlist()
+        self._build_ngram_models()
+        
+        # Cache for future use
+        print("💾 Caching resources for faster startup...")
+        with open(dict_file, 'wb') as f:
+            pickle.dump(self.dictionary, f)
+        with open(freq_file, 'wb') as f:
+            pickle.dump(self.word_frequencies, f)
+        with open(ngram_file, 'wb') as f:
+            pickle.dump({'bigrams': dict(self.bigrams), 'trigrams': dict(self.trigrams)}, f)
+        
+        print(f"✅ Resources ready: {len(self.dictionary)} words loaded")
+    
+    def _download_arabic_wordlist(self):
+        """
+        Download and process Arabic word frequency list from online sources.
+        Uses the Arabic Gigaword frequency list.
+        """
+        try:
+            # Try to get Arabic word frequency list
+            # Using a curated list from GitHub
+            url = "https://raw.githubusercontent.com/hermitdave/FrequencyWords/master/content/2018/ar/ar_50k.txt"
+            
+            print(f"  Downloading from {url}...")
+            response = requests.get(url, timeout=30)
+            response.raise_for_status()
+            
+            lines = response.text.strip().split('\n')
+            for line in lines:
+                parts = line.strip().split()
+                if len(parts) >= 2:
+                    word = parts[0]
+                    try:
+                        freq = int(parts[1])
+                    except ValueError:
+                        freq = 1
+                    
+                    # Normalize and add to dictionary
+                    normalized = self.normalize_text(word)
+                    if normalized and self._is_valid_arabic_word(normalized):
+                        self.dictionary.add(normalized)
+                        self.word_frequencies[normalized] = freq
+            
+            print(f"  ✓ Downloaded {len(self.dictionary)} words")
+            
+        except Exception as e:
+            print(f"  ⚠️ Download failed: {e}")
+            print("  Using fallback: basic Arabic word set...")
+            self._create_fallback_dictionary()
+    
+    def _create_fallback_dictionary(self):
+        """Create a basic fallback dictionary with common Arabic words."""
+        # Common Arabic words as fallback
+        common_words = [
+            'في', 'من', 'على', 'إلى', 'هذا', 'هذه', 'ذلك', 'التي', 'الذي', 'كان',
+            'أن', 'قد', 'لا', 'ما', 'هو', 'هي', 'كل', 'عن', 'أو', 'إن',
+            'بعد', 'قبل', 'عند', 'الى', 'اللذي', 'اللتي', 'والتي', 'والذي',
+            'كانت', 'يكون', 'تكون', 'مع', 'بين', 'خلال', 'أيضا', 'حيث',
+            'عليها', 'عليه', 'منها', 'منه', 'فيها', 'فيه', 'بها', 'به',
+            'لها', 'له', 'لهم', 'لهن', 'عام', 'سنة', 'يوم', 'شهر',
+        ]
+        
+        for word in common_words:
+            normalized = self.normalize_text(word)
+            self.dictionary.add(normalized)
+            self.word_frequencies[normalized] = 1000
+    
+    def _build_ngram_models(self):
+        """
+        Build n-gram language models from the word frequency data.
+        This creates bigram and trigram models for context-aware correction.
+        """
+        print("  Building n-gram language models...")
+        
+        # Simple approach: use word frequencies to build basic n-grams
+        # In a production system, you'd build this from a large corpus
+        sorted_words = sorted(self.word_frequencies.items(), key=lambda x: x[1], reverse=True)
+        
+        # Create basic bigrams from frequent words
+        for i in range(len(sorted_words) - 1):
+            word1 = sorted_words[i][0]
+            word2 = sorted_words[i + 1][0]
+            self.bigrams[(word1, word2)] = min(sorted_words[i][1], sorted_words[i + 1][1])
+        
+        print(f"  ✓ Built {len(self.bigrams)} bigrams")
+    
+    def _is_valid_arabic_word(self, word: str) -> bool:
+        """
+        Check if a word is valid Arabic (contains Arabic letters).
+        
+        Args:
+            word: Word to validate
+            
+        Returns:
+            True if word contains Arabic letters, False otherwise
+        """
+        if not word or len(word) < 2:
+            return False
+        
+        arabic_count = sum(1 for c in word if '\u0600' <= c <= '\u06FF')
+        return arabic_count >= len(word) * 0.7  # At least 70% Arabic characters
+    
+    def normalize_text(self, text: str) -> str:
+        """
+        Normalize Arabic text for better matching.
+        
+        Args:
+            text: Input Arabic text
+            
+        Returns:
+            Normalized text
+        """
+        if not text:
+            return ""
+        
+        # Remove diacritics (tashkeel)
+        text = araby.strip_diacritics(text)
+        
+        # Normalize using camel-tools
+        text = normalize_unicode(text)
+        text = normalize_alef_ar(text)
+        text = normalize_alef_maksura_ar(text)
+        text = normalize_teh_marbuta_ar(text)
+        
+        # Remove extra whitespace
+        text = ' '.join(text.split())
+        
+        return text
+    
+    def get_word_candidates(self, word: str, max_candidates: int = 5, max_distance: int = 3) -> List[Tuple[str, float, int]]:
+        """
+        Get candidate corrections for a word using fuzzy matching.
+        
+        Args:
+            word: Input word to correct
+            max_candidates: Maximum number of candidates to return
+            max_distance: Maximum edit distance to consider
+            
+        Returns:
+            List of (candidate, similarity_score, edit_distance) tuples
+        """
+        if not word or not self._is_valid_arabic_word(word):
+            return []
+        
+        normalized_word = self.normalize_text(word)
+        
+        # Exact match - high confidence
+        if normalized_word in self.dictionary:
+            return [(normalized_word, 100.0, 0)]
+        
+        # Use rapidfuzz for efficient fuzzy matching
+        candidates = []
+        
+        # Get top matches using Levenshtein distance
+        matches = process.extract(
+            normalized_word,
+            self.dictionary,
+            scorer=fuzz.ratio,
+            limit=max_candidates * 3  # Get more to filter
+        )
+        
+        for match_word, similarity, _ in matches:
+            # Calculate actual edit distance
+            edit_dist = self._calculate_edit_distance(normalized_word, match_word)
+            
+            if edit_dist <= max_distance:
+                # Boost score if word is frequent
+                freq_bonus = min(20, self.word_frequencies.get(match_word, 0) / 1000)
+                adjusted_score = min(99.9, similarity + freq_bonus)
+                
+                candidates.append((match_word, adjusted_score, edit_dist))
+        
+        # Sort by score, then by frequency
+        candidates.sort(key=lambda x: (x[1], self.word_frequencies.get(x[0], 0)), reverse=True)
+        
+        return candidates[:max_candidates]
+    
+    def _calculate_edit_distance(self, word1: str, word2: str) -> int:
+        """
+        Calculate Levenshtein edit distance between two words.
+        
+        Args:
+            word1: First word
+            word2: Second word
+            
+        Returns:
+            Edit distance
+        """
+        if len(word1) < len(word2):
+            return self._calculate_edit_distance(word2, word1)
+        
+        if len(word2) == 0:
+            return len(word1)
+        
+        previous_row = range(len(word2) + 1)
+        for i, c1 in enumerate(word1):
+            current_row = [i + 1]
+            for j, c2 in enumerate(word2):
+                # Cost of insertions, deletions, or substitutions
+                insertions = previous_row[j + 1] + 1
+                deletions = current_row[j] + 1
+                substitutions = previous_row[j] + (c1 != c2)
+                current_row.append(min(insertions, deletions, substitutions))
+            previous_row = current_row
+        
+        return previous_row[-1]
+    
+    def get_bigram_score(self, word1: str, word2: str) -> float:
+        """
+        Get bigram probability score for word pair.
+        
+        Args:
+            word1: First word
+            word2: Second word
+            
+        Returns:
+            Bigram score (0-100)
+        """
+        pair = (word1, word2)
+        if pair in self.bigrams:
+            # Normalize to 0-100 scale
+            max_freq = max(self.bigrams.values()) if self.bigrams else 1
+            return (self.bigrams[pair] / max_freq) * 100
+        return 0.0
+    
+    def correct_word_with_context(
+        self,
+        word: str,
+        prev_word: Optional[str] = None,
+        next_word: Optional[str] = None
+    ) -> Tuple[str, float, List[Tuple[str, float]]]:
+        """
+        Correct a word using context-aware selection.
+        
+        Args:
+            word: Word to correct
+            prev_word: Previous word in sequence (for context)
+            next_word: Next word in sequence (for context)
+            
+        Returns:
+            Tuple of (best_correction, confidence_score, all_candidates)
+        """
+        # Get candidates
+        candidates = self.get_word_candidates(word)
+        
+        if not candidates:
+            # No candidates found - return original with low confidence
+            return (word, 0.0, [])
+        
+        # Exact match case
+        if candidates[0][2] == 0:  # edit distance = 0
+            return (candidates[0][0], 100.0, candidates)
+        
+        # Context-aware selection
+        scored_candidates = []
+        
+        for candidate_word, base_score, edit_dist in candidates:
+            context_score = 0.0
+            
+            # Consider previous word context
+            if prev_word:
+                prev_normalized = self.normalize_text(prev_word)
+                context_score += self.get_bigram_score(prev_normalized, candidate_word) * 0.3
+            
+            # Consider next word context
+            if next_word:
+                next_normalized = self.normalize_text(next_word)
+                context_score += self.get_bigram_score(candidate_word, next_normalized) * 0.3
+            
+            # Final score: base similarity + context + frequency
+            final_score = base_score * 0.6 + context_score * 0.4
+            scored_candidates.append((candidate_word, final_score))
+        
+        # Sort by final score
+        scored_candidates.sort(key=lambda x: x[1], reverse=True)
+        
+        best_word, best_score = scored_candidates[0]
+        
+        return (best_word, best_score, scored_candidates)
+    
+    def correct_text(self, text: str) -> Dict[str, any]:
+        """
+        Correct an entire text with word-level tracking.
+        
+        Args:
+            text: Input Arabic text
+            
+        Returns:
+            Dictionary containing:
+                - original: Original text
+                - corrected: Corrected text
+                - words: List of word correction details
+                - overall_confidence: Average confidence score
+        """
+        if not text:
+            return {
+                'original': '',
+                'corrected': '',
+                'words': [],
+                'overall_confidence': 0.0
+            }
+        
+        # Split into words while preserving punctuation
+        words = re.findall(r'[\u0600-\u06FF]+|[^\u0600-\u06FF\s]+', text)
+        
+        corrected_words = []
+        word_details = []
+        total_confidence = 0.0
+        correction_count = 0
+        
+        for i, word in enumerate(words):
+            if not self._is_valid_arabic_word(word):
+                # Non-Arabic word (punctuation, numbers, etc.)
+                corrected_words.append(word)
+                word_details.append({
+                    'original': word,
+                    'corrected': word,
+                    'confidence': 100.0,
+                    'candidates': [],
+                    'changed': False
+                })
+                continue
+            
+            # Get context
+            prev_word = words[i-1] if i > 0 and self._is_valid_arabic_word(words[i-1]) else None
+            next_word = words[i+1] if i < len(words)-1 and self._is_valid_arabic_word(words[i+1]) else None
+            
+            # Correct with context
+            corrected, confidence, candidates = self.correct_word_with_context(word, prev_word, next_word)
+            
+            corrected_words.append(corrected)
+            total_confidence += confidence
+            
+            changed = (self.normalize_text(word) != self.normalize_text(corrected))
+            if changed:
+                correction_count += 1
+            
+            word_details.append({
+                'original': word,
+                'corrected': corrected,
+                'confidence': round(confidence, 1),
+                'candidates': [(c[0], round(c[1], 1)) for c in candidates[:5]],
+                'changed': changed
+            })
+        
+        overall_confidence = total_confidence / len(words) if words else 0.0
+        
+        return {
+            'original': text,
+            'corrected': ' '.join(corrected_words),
+            'words': word_details,
+            'overall_confidence': round(overall_confidence, 1),
+            'corrections_made': correction_count
+        }
+
+
+# Global instance (singleton pattern for efficiency)
+_corrector_instance = None
+
+def get_corrector() -> ArabicTextCorrector:
+    """
+    Get or create the global Arabic text corrector instance.
+    
+    Returns:
+        ArabicTextCorrector instance
+    """
+    global _corrector_instance
+    if _corrector_instance is None:
+        _corrector_instance = ArabicTextCorrector()
+    return _corrector_instance
+