""" Direct Extraction Engine using PyMuPDF Handles direct text and structure extraction from editable PDFs without OCR. This provides much faster processing and perfect accuracy for documents with extractable text. """ import os import logging import fitz # PyMuPDF import uuid from pathlib import Path from typing import Dict, List, Optional, Tuple, Any, Union from datetime import datetime import re from ..models.unified_document import ( UnifiedDocument, DocumentElement, Page, DocumentMetadata, BoundingBox, StyleInfo, TableData, TableCell, Dimensions, ElementType, ProcessingTrack ) logger = logging.getLogger(__name__) class DirectExtractionEngine: """ Engine for direct text extraction from editable PDFs using PyMuPDF. This engine provides: - Fast text extraction with exact positioning - Font and style information preservation - Table structure detection - Image extraction with coordinates - Hyperlink and annotation extraction """ def __init__(self, enable_table_detection: bool = True, enable_image_extraction: bool = True, min_table_rows: int = 2, min_table_cols: int = 2, min_image_area: float = 200.0, # Preprocessing pipeline options enable_content_sanitization: bool = True, enable_hidden_layer_removal: bool = True, enable_whiteout_detection: bool = True, whiteout_iou_threshold: float = 0.8, enable_page_number_filter: bool = True, enable_garble_detection: bool = True, garble_ocr_fallback_threshold: float = 0.1): """ Initialize the extraction engine. Args: enable_table_detection: Whether to detect and extract tables enable_image_extraction: Whether to extract images min_table_rows: Minimum rows for table detection min_table_cols: Minimum columns for table detection min_image_area: Minimum image area in pixels squared (default 200) Images smaller than this are filtered as decorations Preprocessing pipeline options: enable_content_sanitization: Run clean_contents() to fix malformed PDF streams enable_hidden_layer_removal: Remove content from hidden OCG layers enable_whiteout_detection: Detect and filter text covered by white rectangles whiteout_iou_threshold: IoU threshold for white-out detection (default 0.8) enable_page_number_filter: Filter out detected page numbers enable_garble_detection: Detect garbled text (cid:xxxx patterns) garble_ocr_fallback_threshold: Garble rate threshold to recommend OCR fallback """ self.enable_table_detection = enable_table_detection self.enable_image_extraction = enable_image_extraction self.min_table_rows = min_table_rows self.min_table_cols = min_table_cols self.min_image_area = min_image_area # Preprocessing pipeline options self.enable_content_sanitization = enable_content_sanitization self.enable_hidden_layer_removal = enable_hidden_layer_removal self.enable_whiteout_detection = enable_whiteout_detection self.whiteout_iou_threshold = whiteout_iou_threshold self.enable_page_number_filter = enable_page_number_filter self.enable_garble_detection = enable_garble_detection self.garble_ocr_fallback_threshold = garble_ocr_fallback_threshold def extract(self, file_path: Union[str, Path], output_dir: Optional[Path] = None) -> UnifiedDocument: """ Extract content from PDF file to UnifiedDocument format. Args: file_path: Path to PDF file (string or Path object) output_dir: Optional directory to save extracted images. If not provided, creates a temporary directory in storage/results/{document_id}/ Returns: UnifiedDocument with extracted content """ # Ensure file_path is a Path object if isinstance(file_path, str): file_path = Path(file_path) start_time = datetime.now() document_id = str(uuid.uuid4())[:8] # Short ID for cleaner paths try: doc = fitz.open(str(file_path)) # If no output_dir provided, create default directory for image extraction if output_dir is None and self.enable_image_extraction: # Create temporary directory in storage/results default_output_dir = Path("storage/results") / document_id default_output_dir.mkdir(parents=True, exist_ok=True) output_dir = default_output_dir logger.debug(f"Created default output directory: {output_dir}") # Extract document metadata metadata = self._extract_metadata(file_path, doc, start_time) # Extract pages pages = [] for page_num in range(len(doc)): logger.info(f"Extracting page {page_num + 1}/{len(doc)}") page = self._extract_page( doc[page_num], page_num + 1, document_id, output_dir, doc # Pass doc for covering image detection ) pages.append(page) doc.close() # Calculate processing time processing_time = (datetime.now() - start_time).total_seconds() metadata.processing_time = processing_time logger.info(f"Direct extraction completed in {processing_time:.2f}s") return UnifiedDocument( document_id=document_id, metadata=metadata, pages=pages ) except Exception as e: logger.error(f"Error during direct extraction: {e}") # Return partial result with error information processing_time = (datetime.now() - start_time).total_seconds() if 'metadata' not in locals(): metadata = DocumentMetadata( filename=file_path.name, file_type="pdf", file_size=file_path.stat().st_size if file_path.exists() else 0, created_at=datetime.now(), processing_track=ProcessingTrack.DIRECT, processing_time=processing_time ) return UnifiedDocument( document_id=document_id, metadata=metadata, pages=pages if 'pages' in locals() else [], processing_errors=[{ "error": str(e), "type": type(e).__name__ }] ) def _extract_metadata(self, file_path: Path, doc: fitz.Document, start_time: datetime) -> DocumentMetadata: """Extract document metadata""" pdf_metadata = doc.metadata return DocumentMetadata( filename=file_path.name, file_type="pdf", file_size=file_path.stat().st_size, created_at=start_time, processing_track=ProcessingTrack.DIRECT, processing_time=0.0, # Will be updated later title=pdf_metadata.get("title"), author=pdf_metadata.get("author"), subject=pdf_metadata.get("subject"), keywords=pdf_metadata.get("keywords", "").split(",") if pdf_metadata.get("keywords") else None, producer=pdf_metadata.get("producer"), creator=pdf_metadata.get("creator"), creation_date=self._parse_pdf_date(pdf_metadata.get("creationDate")), modification_date=self._parse_pdf_date(pdf_metadata.get("modDate")) ) def _parse_pdf_date(self, date_str: str) -> Optional[datetime]: """Parse PDF date string to datetime""" if not date_str: return None try: # PDF date format: D:YYYYMMDDHHmmSSOHH'mm # Example: D:20240101120000+09'00 if date_str.startswith("D:"): date_str = date_str[2:] # Extract just the date/time part (first 14 characters) if len(date_str) >= 14: date_part = date_str[:14] return datetime.strptime(date_part, "%Y%m%d%H%M%S") except: pass return None def _extract_page(self, page: fitz.Page, page_num: int, document_id: str, output_dir: Optional[Path], doc: fitz.Document = None) -> Page: """Extract content from a single page with preprocessing pipeline.""" elements = [] element_counter = 0 # ===================================================================== # PREPROCESSING PIPELINE # ===================================================================== # Step 1: Run preprocessing (sanitization, white-out detection, covering images) preprocess_result = self._preprocess_page(page, page_num, doc) covered_bboxes = preprocess_result.get('covered_word_bboxes', []) # Get page-level metadata (for final Page metadata) drawings = page.get_drawings() links = page.get_links() # Get page dimensions rect = page.rect dimensions = Dimensions( width=rect.width, height=rect.height, dpi=72 # PDF standard DPI ) # Extract tables first (if enabled) to get table regions table_bboxes = [] if self.enable_table_detection: try: # Try native table detection (PyMuPDF 1.23.0+) tables = page.find_tables() for table_idx, table in enumerate(tables): element = self._process_native_table( table, page, page_num, element_counter ) if element and element.bbox: elements.append(element) table_bboxes.append(element.bbox) element_counter += 1 except AttributeError: # Fallback to positional table detection logger.debug("Native table detection not available, using positional detection") table_elements = self._detect_tables_by_position(page, page_num, element_counter) for elem in table_elements: if elem.bbox: table_bboxes.append(elem.bbox) elements.extend(table_elements) element_counter += len(table_elements) # Extract text blocks with formatting (sort=True for reading order) # Filter out lines that overlap with table regions OR covered by white-out text_dict = page.get_text("dict", sort=True) for block_idx, block in enumerate(text_dict.get("blocks", [])): if block.get("type") == 0: # Text block element = self._process_text_block( block, page_num, element_counter, table_bboxes ) if element: # Step 1.3: Skip text covered by white-out rectangles if covered_bboxes and element.bbox: if self._is_text_in_covered_regions(element.bbox, covered_bboxes): logger.debug(f"Skipping white-out covered text: {element.element_id}") continue elements.append(element) element_counter += 1 # Extract images (if enabled) # Pass covering_images and covering_rect_bboxes to filter out redaction/covering rectangles if self.enable_image_extraction: covering_images = preprocess_result.get('covering_images', []) covering_rect_bboxes = preprocess_result.get('covering_rect_bboxes', []) image_elements = self._extract_images( page, page_num, document_id, element_counter, output_dir, covering_images=covering_images, covered_bboxes=covering_rect_bboxes # Pass actual covering vector rectangles ) elements.extend(image_elements) element_counter += len(image_elements) # Extract vector graphics (charts, diagrams) from drawing commands # Pass table_bboxes to filter out table border drawings before clustering if self.enable_image_extraction: vector_elements = self._extract_vector_graphics( page, page_num, document_id, element_counter, output_dir, table_bboxes=table_bboxes ) elements.extend(vector_elements) element_counter += len(vector_elements) # Extract hyperlinks links = page.get_links() for link_idx, link in enumerate(links): # Create link annotation element if it has URI if link.get("uri"): from_rect = link.get("from") if from_rect: element = DocumentElement( element_id=f"link_{page_num}_{element_counter}", type=ElementType.REFERENCE, content={"uri": link["uri"], "type": "hyperlink"}, bbox=BoundingBox( x0=from_rect.x0, y0=from_rect.y0, x1=from_rect.x1, y1=from_rect.y1 ), metadata={"link_type": "external" if link["uri"].startswith("http") else "internal"} ) elements.append(element) element_counter += 1 # PyMuPDF's sort=True already provides good reading order for multi-column layouts # (top-to-bottom, left-to-right within each row). We don't need to re-sort. # NOTE: If sort=True is not used in get_text(), uncomment the line below: # elements = self._sort_elements_for_reading_order(elements, dimensions) # Deduplicate: Remove CHART elements that overlap with TABLE elements # (Tables have structured data, so they take priority over vector graphics) elements = self._deduplicate_table_chart_overlap(elements) # Post-process elements for header/footer detection and structure elements = self._detect_headers_footers(elements, dimensions) elements = self._build_section_hierarchy(elements) elements = self._build_nested_lists(elements) # ===================================================================== # POST-PROCESSING PIPELINE # ===================================================================== # Step 2.3: Filter page numbers elements = self._filter_page_numbers(elements, dimensions.height) # Step 3.2-3.3: Garble detection and OCR fallback recommendation covering_images = preprocess_result.get('covering_images', []) page_metadata = { "has_drawings": len(drawings) > 0, "drawing_count": len(drawings), "link_count": len(links), "preprocessing": { "sanitized": preprocess_result.get('sanitized', False), "whiteout_regions_found": len(covered_bboxes) - len(covering_images), # Vector rects only "covering_images_found": len(covering_images), "covering_images": covering_images # Full details for debugging } } # Calculate garble rate for the page if self.enable_garble_detection: full_text = ' '.join( elem.get_text() if hasattr(elem, 'get_text') else str(elem.content) for elem in elements if elem.type in [ElementType.TEXT, ElementType.PARAGRAPH, ElementType.TITLE] ) garble_rate = self._calculate_garble_rate(full_text) page_metadata['garble_rate'] = garble_rate page_metadata['needs_ocr_fallback'] = self._should_fallback_to_ocr(full_text, page_num) return Page( page_number=page_num, elements=elements, dimensions=dimensions, metadata=page_metadata ) def _sort_elements_for_reading_order(self, elements: List[DocumentElement], dimensions: Dimensions) -> List[DocumentElement]: """ Sort elements by reading order, handling multi-column layouts. For multi-column layouts (e.g., two-column documents), this ensures elements are ordered correctly: top-to-bottom, then left-to-right within each row. Args: elements: List of document elements dimensions: Page dimensions Returns: Sorted list of elements in reading order """ if not elements: return elements # Detect if page has multi-column layout text_elements = [e for e in elements if e.bbox and e.is_text] if len(text_elements) < 3: # Too few elements to determine layout, just sort by Y position return sorted(elements, key=lambda e: (e.bbox.y0 if e.bbox else 0, e.bbox.x0 if e.bbox else 0)) # Cluster x-positions to detect columns x_positions = [e.bbox.x0 for e in text_elements] columns = self._detect_columns(x_positions, dimensions.width) if len(columns) <= 1: # Single column layout - simple top-to-bottom sort logger.debug(f"Detected single-column layout") return sorted(elements, key=lambda e: (e.bbox.y0 if e.bbox else 0, e.bbox.x0 if e.bbox else 0)) logger.debug(f"Detected {len(columns)}-column layout at x positions: {[f'{x:.1f}' for x in columns]}") # Multi-column layout - use newspaper-style reading order # (complete left column, then right column, etc.) # This is more appropriate for technical documents and data sheets element_data = [] for elem in elements: if not elem.bbox: element_data.append((elem, 0, 0)) continue # Find which column this element belongs to col_idx = 0 min_dist = float('inf') for i, col_x in enumerate(columns): dist = abs(elem.bbox.x0 - col_x) if dist < min_dist: min_dist = dist col_idx = i element_data.append((elem, col_idx, elem.bbox.y0)) # Sort by: column first, then Y position within column # This gives newspaper-style reading: complete column 1, then column 2, etc. element_data.sort(key=lambda x: (x[1], x[2])) logger.debug(f"Using newspaper-style column reading order (column by column, top to bottom)") return [e[0] for e in element_data] def _detect_columns(self, x_positions: List[float], page_width: float) -> List[float]: """ Detect column positions from x-coordinates of text elements. Args: x_positions: List of x-coordinates (left edges of text) page_width: Page width in points Returns: List of column x-positions (sorted left to right) """ if not x_positions: return [] # Cluster x-positions to find column starts # Use k-means-like approach: find groups of x-positions threshold = page_width * 0.15 # 15% of page width as clustering threshold sorted_x = sorted(set(x_positions)) if not sorted_x: return [] clusters = [[sorted_x[0]]] for x in sorted_x[1:]: # Check if x belongs to current cluster cluster_center = sum(clusters[-1]) / len(clusters[-1]) if abs(x - cluster_center) < threshold: clusters[-1].append(x) else: # Start new cluster clusters.append([x]) # Return average x position of each cluster (column start) column_positions = [sum(cluster) / len(cluster) for cluster in clusters] # Filter out columns that are too close to each other min_column_width = page_width * 0.2 # Columns must be at least 20% of page width apart filtered_columns = [column_positions[0]] for col_x in column_positions[1:]: if col_x - filtered_columns[-1] >= min_column_width: filtered_columns.append(col_x) return filtered_columns def _detect_headers_footers(self, elements: List[DocumentElement], dimensions: Dimensions) -> List[DocumentElement]: """Detect and mark header/footer elements based on page position""" page_height = dimensions.height header_threshold = page_height * 0.1 # Top 10% of page footer_threshold = page_height * 0.9 # Bottom 10% of page for elem in elements: # Skip non-text elements if not elem.is_text: continue # Check if element is in header region if elem.bbox.y1 <= header_threshold: # Only mark as header if it's short text if isinstance(elem.content, str) and len(elem.content) < 200: elem.type = ElementType.HEADER elem.metadata['is_page_header'] = True # Check if element is in footer region elif elem.bbox.y0 >= footer_threshold: # Short text in footer region if isinstance(elem.content, str) and len(elem.content) < 200: elem.type = ElementType.FOOTER elem.metadata['is_page_footer'] = True return elements def _build_section_hierarchy(self, elements: List[DocumentElement]) -> List[DocumentElement]: """Build hierarchical section structure based on font sizes""" # Collect all headers with their font sizes headers = [] for elem in elements: if elem.type in [ElementType.TITLE, ElementType.HEADER]: # Get average font size from style font_size = 12.0 # Default if elem.style and elem.style.font_size: font_size = elem.style.font_size headers.append((elem, font_size)) if not headers: return elements # Sort headers by font size to determine hierarchy levels font_sizes = sorted(set(size for _, size in headers), reverse=True) size_to_level = {size: level for level, size in enumerate(font_sizes, 1)} # Assign section levels to headers for elem, font_size in headers: level = size_to_level.get(font_size, 1) elem.metadata['section_level'] = level elem.metadata['font_size'] = font_size # Build parent-child relationships between headers header_stack = [] # Stack of (element, level) for elem, font_size in headers: level = elem.metadata['section_level'] # Pop headers that are at same or lower level (larger font) while header_stack and header_stack[-1][1] >= level: header_stack.pop() # Set parent header if header_stack: parent = header_stack[-1][0] elem.metadata['parent_section'] = parent.element_id if 'child_sections' not in parent.metadata: parent.metadata['child_sections'] = [] parent.metadata['child_sections'].append(elem.element_id) header_stack.append((elem, level)) # Link content to nearest preceding header at same or higher level current_header = None for elem in elements: if elem.type in [ElementType.TITLE, ElementType.HEADER]: current_header = elem elif current_header and elem.type not in [ElementType.HEADER, ElementType.FOOTER]: elem.metadata['section_id'] = current_header.element_id return elements def _build_nested_lists(self, elements: List[DocumentElement]) -> List[DocumentElement]: """Build nested list structure from flat list items""" # Group list items list_items = [e for e in elements if e.type == ElementType.LIST_ITEM] if not list_items: return elements # Sort by position (top to bottom) list_items.sort(key=lambda e: (e.bbox.y0, e.bbox.x0)) # Detect indentation levels based on x position x_positions = [item.bbox.x0 for item in list_items] if not x_positions: return elements min_x = min(x_positions) indent_unit = 20 # Typical indent size in points # Assign nesting levels for item in list_items: indent = item.bbox.x0 - min_x level = int(indent / indent_unit) item.metadata['list_level'] = level # Build parent-child relationships item_stack = [] # Stack of (element, level) for item in list_items: level = item.metadata.get('list_level', 0) # Pop items at same or deeper level while item_stack and item_stack[-1][1] >= level: item_stack.pop() # Set parent if item_stack: parent = item_stack[-1][0] item.metadata['parent_item'] = parent.element_id if 'children' not in parent.metadata: parent.metadata['children'] = [] parent.metadata['children'].append(item.element_id) # Also add to actual children list parent.children.append(item) item_stack.append((item, level)) return elements def _process_text_block(self, block: Dict, page_num: int, counter: int, table_bboxes: List[BoundingBox] = None) -> Optional[DocumentElement]: """ Process a text block into a DocumentElement. Args: block: Text block from PyMuPDF page_num: Page number counter: Element counter table_bboxes: List of table bounding boxes to filter overlapping lines Returns: DocumentElement or None if all lines overlap with tables """ if table_bboxes is None: table_bboxes = [] # Extract text content and span information # Filter out lines that significantly overlap with table regions text_parts = [] styles = [] span_children = [] # Store span-level children for inline styling span_counter = 0 valid_line_bboxes = [] # Track bboxes of valid lines for overall bbox calculation for line in block.get("lines", []): line_bbox_data = line.get("bbox", [0, 0, 0, 0]) # Check if this line overlaps with any table region line_overlaps_table = False for table_bbox in table_bboxes: overlap_x0 = max(line_bbox_data[0], table_bbox.x0) overlap_y0 = max(line_bbox_data[1], table_bbox.y0) overlap_x1 = min(line_bbox_data[2], table_bbox.x1) overlap_y1 = min(line_bbox_data[3], table_bbox.y1) if overlap_x0 < overlap_x1 and overlap_y0 < overlap_y1: # Calculate overlap ratio line_height = line_bbox_data[3] - line_bbox_data[1] overlap_height = overlap_y1 - overlap_y0 if line_height > 0: overlap_ratio = overlap_height / line_height if overlap_ratio >= 0.5: # Line significantly overlaps with table line_overlaps_table = True break if line_overlaps_table: continue # Skip this line # Process valid line valid_line_bboxes.append(line_bbox_data) for span in line.get("spans", []): text = span.get("text", "") if text: text_parts.append(text) # Extract style information style = StyleInfo( font_name=span.get("font"), font_size=span.get("size"), font_weight="bold" if span.get("flags", 0) & 2**4 else "normal", font_style="italic" if span.get("flags", 0) & 2**1 else "normal", text_color=span.get("color") ) styles.append(style) # Create span child element for inline styling span_bbox_data = span.get("bbox", [0, 0, 0, 0]) span_bbox = BoundingBox( x0=span_bbox_data[0], y0=span_bbox_data[1], x1=span_bbox_data[2], y1=span_bbox_data[3] ) span_element = DocumentElement( element_id=f"span_{page_num}_{counter}_{span_counter}", type=ElementType.TEXT, # Spans are always text content=text, bbox=span_bbox, style=style, confidence=1.0, metadata={"span_index": span_counter} ) span_children.append(span_element) span_counter += 1 if not text_parts: return None # All lines overlapped with tables full_text = "".join(text_parts) # Calculate bbox from valid lines only if valid_line_bboxes: min_x0 = min(b[0] for b in valid_line_bboxes) min_y0 = min(b[1] for b in valid_line_bboxes) max_x1 = max(b[2] for b in valid_line_bboxes) max_y1 = max(b[3] for b in valid_line_bboxes) bbox = BoundingBox(x0=min_x0, y0=min_y0, x1=max_x1, y1=max_y1) else: # Fallback to original bbox if no valid lines found bbox_data = block.get("bbox", [0, 0, 0, 0]) bbox = BoundingBox(x0=bbox_data[0], y0=bbox_data[1], x1=bbox_data[2], y1=bbox_data[3]) # Determine element type based on content and style element_type = self._infer_element_type(full_text, styles) # Use the most common style for the block if styles: block_style = styles[0] # Could be improved with style merging else: block_style = None return DocumentElement( element_id=f"text_{page_num}_{counter}", type=element_type, content=full_text, bbox=bbox, style=block_style, confidence=1.0, # Direct extraction has perfect confidence children=span_children # Store span children for inline styling ) def _infer_element_type(self, text: str, styles: List[StyleInfo]) -> ElementType: """Infer element type based on text content and styling""" text_lower = text.lower().strip() # Check for common patterns if len(text_lower) < 100 and styles: # Short text with large font might be title/header avg_size = sum(s.font_size or 12 for s in styles) / len(styles) if avg_size > 16: return ElementType.TITLE elif avg_size > 14: return ElementType.HEADER # Check for list patterns if re.match(r'^[\d•·▪▫◦‣⁃]\s', text_lower): return ElementType.LIST_ITEM # Check for page numbers if re.match(r'^page\s+\d+|^\d+\s*$|^-\s*\d+\s*-$', text_lower): return ElementType.PAGE_NUMBER # Check for footnote patterns if re.match(r'^[\[\d+\]]|^\d+\)', text_lower): return ElementType.FOOTNOTE # Default to paragraph for longer text, text for shorter return ElementType.PARAGRAPH if len(text) > 150 else ElementType.TEXT def _is_likely_chart(self, data: list, table) -> bool: """ Detect if a "table" detected by find_tables() is actually a chart/graph. Charts often get misclassified as tables because they have grid lines. Characteristics of a chart misclassified as table: 1. High percentage of empty cells (>60%) 2. Content patterns that look like axis labels (numbers, units like °C, %, etc.) 3. Single cell contains multi-line text with chart-like patterns 4. Cell content contains typical chart axis patterns Args: data: Extracted table data (list of lists) table: PyMuPDF table object Returns: True if the table is likely a chart """ if not data: return False # Count total cells and empty cells total_cells = 0 empty_cells = 0 multi_line_cells = 0 axis_pattern_cells = 0 # Patterns that suggest chart axis labels import re axis_patterns = [ r'^-?\d+$', # Simple numbers (axis ticks) r'^-?\d+\.?\d*$', # Decimal numbers r'°[CF]', # Temperature units r'%$', # Percentage r'\bppm\b', # Parts per million r'\bmin\b', # Minutes r'\bsec\b', # Seconds r'\bTime\b', # Time axis label r'\bTemperature\b', # Temperature axis label r'[Aa]xis', # Axis label ] for row in data: for cell in row: total_cells += 1 cell_text = str(cell).strip() if cell else "" if not cell_text: empty_cells += 1 else: # Check for multi-line content if '\n' in cell_text: multi_line_cells += 1 # Check for axis patterns for pattern in axis_patterns: if re.search(pattern, cell_text, re.IGNORECASE): axis_pattern_cells += 1 break # Calculate metrics empty_ratio = empty_cells / total_cells if total_cells > 0 else 0 non_empty_cells = total_cells - empty_cells # Count cells with meaningful table content (units, CJK, technical terms) table_content_cells = 0 table_content_patterns = [ r'[一-龥ぁ-んァ-ン]', # CJK characters (Chinese, Japanese) r'\b(Wt%|MPa|GPa|W/mK|ppm|cps|rpm)\b', # Technical units r'\b(RT|TMA|DMA)\b', # Technical abbreviations r'±', # Plus-minus symbol (common in specs) r'\d+\s*[x×]\s*\d+', # Dimensions like "10x10" ] for row in data: for cell in row: cell_text = str(cell).strip() if cell else "" if cell_text: for pattern in table_content_patterns: if re.search(pattern, cell_text): table_content_cells += 1 break # Decision criteria for chart detection: # Tables with technical/CJK content are likely real tables, not charts if table_content_cells >= 5: logger.debug(f"Table detection: {table_content_cells} cells with table-like content") return False # If table has many rows with data, it's likely a real table rows_with_content = sum(1 for row in data if any(str(cell).strip() for cell in row if cell)) if rows_with_content >= 5 and non_empty_cells >= 10: logger.debug(f"Table detection: {rows_with_content} rows with content, {non_empty_cells} non-empty cells") return False # 1. Extremely high empty cell ratio (>90%) suggests it's a chart grid if empty_ratio > 0.9: logger.debug(f"Chart detection: very high empty ratio {empty_ratio:.2f} (>90%)") return True # 2. High empty ratio + many axis patterns suggests chart if empty_ratio > 0.7 and axis_pattern_cells >= 5: logger.debug(f"Chart detection: empty ratio {empty_ratio:.2f} + {axis_pattern_cells} axis patterns") return True # 3. Multi-line cell with axis patterns in first cell (often chart legend text) if multi_line_cells >= 1 and axis_pattern_cells >= 3: first_cell = str(data[0][0]).strip() if data and data[0] else "" if '\n' in first_cell and len(first_cell.split('\n')) >= 5: logger.debug(f"Chart detection: first cell has {len(first_cell.split(chr(10)))} lines with axis patterns") return True return False def _process_native_table(self, table, fitz_page, page_num: int, counter: int) -> Optional[DocumentElement]: """ Process a natively detected table with proper merged cell handling. Uses PyMuPDF's table.rows to detect cell spans: - table.rows provides per-row cell info where None indicates merged positions - We calculate row_span/col_span by counting consecutive None values """ try: # Extract table data (text content) data = table.extract() if not data or len(data) < self.min_table_rows: return None # Check if this "table" is actually a chart (misclassified by find_tables) if self._is_likely_chart(data, table): logger.info(f"Skipping table_{page_num}_{counter} - detected as chart (not table)") return None # Get table bounding box bbox_data = table.bbox bbox = BoundingBox( x0=bbox_data[0], y0=bbox_data[1], x1=bbox_data[2], y1=bbox_data[3] ) # Get table dimensions num_rows = table.row_count num_cols = table.col_count if num_cols < self.min_table_cols: return None # Build cell grid from table.rows # Each row has .cells which is a list of bbox tuples or None for merged cells table_rows = getattr(table, 'rows', None) # Create a 2D grid to store cell bboxes (None = merged/covered) cell_grid = [[None for _ in range(num_cols)] for _ in range(num_rows)] if table_rows: for row_idx, row in enumerate(table_rows): row_cells = row.cells if hasattr(row, 'cells') else [] for col_idx, cell_bbox in enumerate(row_cells): if col_idx < num_cols: cell_grid[row_idx][col_idx] = cell_bbox # Create a 2D grid to track which cells are covered by merges # covered[row][col] = (owner_row, owner_col) if covered, None if actual cell covered = [[None for _ in range(num_cols)] for _ in range(num_rows)] # Calculate spans for each cell by analyzing None patterns cell_spans = {} # (row, col) -> (row_span, col_span, cell_bbox) for row_idx in range(num_rows): for col_idx in range(num_cols): cell_bbox = cell_grid[row_idx][col_idx] if cell_bbox is None: # This position is covered by a merged cell - skip continue if covered[row_idx][col_idx] is not None: # Already marked as covered continue # This is an actual cell - calculate its span # Find col_span: count consecutive None values to the right col_span = 1 for c in range(col_idx + 1, num_cols): if cell_grid[row_idx][c] is None and covered[row_idx][c] is None: col_span += 1 else: break # Find row_span: count consecutive None values below # (checking the same column range as col_span) row_span = 1 for r in range(row_idx + 1, num_rows): # Check if all cells in this row's span range are None all_none = True for c in range(col_idx, col_idx + col_span): if c < num_cols: if cell_grid[r][c] is not None or covered[r][c] is not None: all_none = False break if all_none: row_span += 1 else: break # Store the span info cell_spans[(row_idx, col_idx)] = ( row_span, col_span, BoundingBox(x0=cell_bbox[0], y0=cell_bbox[1], x1=cell_bbox[2], y1=cell_bbox[3]) ) # Mark covered positions for dr in range(row_span): for dc in range(col_span): if dr == 0 and dc == 0: continue cr, cc = row_idx + dr, col_idx + dc if cr < num_rows and cc < num_cols: covered[cr][cc] = (row_idx, col_idx) # Extract column widths and row heights from actual cell rectangles column_widths = [] row_heights = [] # Collect unique X and Y boundaries from non-None cells x_boundaries = set() y_boundaries = set() for row_idx in range(num_rows): for col_idx in range(num_cols): cell = cell_grid[row_idx][col_idx] if cell is not None: x_boundaries.add(round(cell[0], 1)) # x0 x_boundaries.add(round(cell[2], 1)) # x1 y_boundaries.add(round(cell[1], 1)) # y0 y_boundaries.add(round(cell[3], 1)) # y1 sorted_x = sorted(x_boundaries) sorted_y = sorted(y_boundaries) if len(sorted_x) >= 2: column_widths = [sorted_x[i+1] - sorted_x[i] for i in range(len(sorted_x)-1)] if len(sorted_y) >= 2: row_heights = [sorted_y[i+1] - sorted_y[i] for i in range(len(sorted_y)-1)] # Create table cells with proper span information cells = [] for row_idx in range(num_rows): row_data = data[row_idx] if row_idx < len(data) else [] for col_idx in range(num_cols): # Skip cells that are covered by a merged cell if covered[row_idx][col_idx] is not None: continue # Skip if not an actual cell if cell_grid[row_idx][col_idx] is None: continue # Get cell content cell_text = row_data[col_idx] if col_idx < len(row_data) else "" # Get span info row_span, col_span, cell_bbox = cell_spans.get( (row_idx, col_idx), (1, 1, None) ) cells.append(TableCell( row=row_idx, col=col_idx, row_span=row_span, col_span=col_span, content=str(cell_text) if cell_text else "", bbox=cell_bbox )) # Try to detect visual column and row boundaries from page drawings # This is more accurate than PyMuPDF's column detection for complex tables visual_boundaries = self._detect_visual_column_boundaries( fitz_page, bbox_data, column_widths ) # Use table.cells (flat list of bboxes) for more accurate row detection raw_table_cells = getattr(table, 'cells', None) row_boundaries = self._detect_visual_row_boundaries( fitz_page, bbox_data, raw_table_cells ) if visual_boundaries: # Remap cells to visual columns and rows cells, column_widths, num_cols, num_rows = self._remap_cells_to_visual_columns( cells, column_widths, num_rows, num_cols, visual_boundaries, row_boundaries ) else: # Fallback to narrow column merging (doesn't modify rows) cells, column_widths, num_cols = self._merge_narrow_columns( cells, column_widths, num_rows, num_cols, min_column_width=10.0 ) # Create table data table_data = TableData( rows=num_rows, cols=num_cols, cells=cells, headers=data[0] if data else None ) # Store metadata metadata = {} if column_widths: metadata["column_widths"] = column_widths if row_heights: metadata["row_heights"] = row_heights # Add merge statistics for debugging merged_cells_count = sum(1 for c in cells if c.row_span > 1 or c.col_span > 1) if merged_cells_count > 0: metadata["merged_cell_count"] = merged_cells_count logger.info(f"Table {page_num}_{counter}: {len(cells)} cells (grid: {num_rows}x{num_cols}), {merged_cells_count} merged") metadata = metadata if metadata else None return DocumentElement( element_id=f"table_{page_num}_{counter}", type=ElementType.TABLE, content=table_data, bbox=bbox, confidence=1.0, metadata=metadata ) except Exception as e: logger.error(f"Error processing native table: {e}") import traceback logger.error(traceback.format_exc()) return None def _merge_narrow_columns( self, cells: List[TableCell], column_widths: List[float], num_rows: int, num_cols: int, min_column_width: float = 10.0 ) -> Tuple[List[TableCell], List[float], int]: """ Merge narrow empty columns (border artifacts) with adjacent content columns. PyMuPDF sometimes detects table border lines as separate columns, resulting in many ~5pt wide columns. This method: 1. Identifies which columns have actual content 2. Uses EMPTY narrow columns as separators between logical column groups 3. Merges each group (narrow cols with content + wide col) into one logical column Args: cells: List of TableCell objects column_widths: List of column widths num_rows: Number of rows num_cols: Number of columns min_column_width: Minimum width to consider as real column (default 10pt) Returns: Tuple of (merged_cells, merged_widths, new_num_cols) """ if not column_widths or len(column_widths) != num_cols: return cells, column_widths, num_cols # Count narrow columns narrow_count = sum(1 for w in column_widths if w < min_column_width) if narrow_count == 0: return cells, column_widths, num_cols # Determine which columns have content cols_with_content = set() for cell in cells: if cell.content and cell.content.strip(): # Mark all columns this cell spans for c in range(cell.col, cell.col + cell.col_span): if c < num_cols: cols_with_content.add(c) logger.info(f"Columns with content: {sorted(cols_with_content)}") # Identify column groups separated by EMPTY narrow columns # Strategy: empty narrow columns act as separators col_groups = [] # List of lists, each inner list is columns in a group current_group = [] for col_idx in range(num_cols): width = column_widths[col_idx] is_narrow = width < min_column_width has_content = col_idx in cols_with_content if is_narrow and not has_content: # Empty narrow column = separator if current_group: col_groups.append(current_group) current_group = [] else: # Content column or narrow column with content current_group.append(col_idx) # Don't forget the last group if current_group: col_groups.append(current_group) logger.info(f"Column groups: {col_groups}") if len(col_groups) == num_cols: # No grouping possible return cells, column_widths, num_cols # Build column mapping: old_col -> new_col col_mapping = {} new_widths = [] for new_col_idx, group in enumerate(col_groups): group_width = sum(column_widths[c] for c in group) # Add width of separators between previous group and this one if new_col_idx > 0 and group: prev_group = col_groups[new_col_idx - 1] if prev_group: # Add separator widths for c in range(prev_group[-1] + 1, group[0]): group_width += column_widths[c] new_widths.append(group_width) for old_col in group: col_mapping[old_col] = new_col_idx new_num_cols = len(col_groups) logger.info(f"Column reduction: {num_cols} -> {new_num_cols}") logger.debug(f"Column mapping: {col_mapping}") # Remap cells to new column indices # Group cells by (row, new_col) to handle merging cell_map = {} # (row, new_col) -> list of cells for cell in cells: new_col = col_mapping.get(cell.col) if new_col is None: # Column was a separator - skip this cell continue key = (cell.row, new_col) if key not in cell_map: cell_map[key] = [] cell_map[key].append(cell) # Create merged cells merged_cells = [] processed = set() for (row, new_col), cell_list in sorted(cell_map.items()): if (row, new_col) in processed: continue # Sort cells by original column to maintain left-to-right order cell_list.sort(key=lambda c: c.col) # Collect all non-empty content from cells in this position contents = [] for c in cell_list: if c.content and c.content.strip(): contents.append(c.content.strip()) # Join contents with newline (for multi-column merged data) merged_content = '\n'.join(contents) if contents else '' # Use the first cell with content for span calculation content_cell = None for c in cell_list: if c.content and c.content.strip(): content_cell = c break if content_cell is None: content_cell = cell_list[0] # Calculate new col_span by mapping old span to new columns old_col_start = content_cell.col old_col_end = old_col_start + content_cell.col_span - 1 new_col_start = col_mapping.get(old_col_start, new_col) new_col_end = col_mapping.get(old_col_end, new_col_start) new_col_span = max(1, new_col_end - new_col_start + 1) # Merge bbox from all cells in this position bbox = content_cell.bbox for c in cell_list: if c.bbox and bbox: bbox = BoundingBox( x0=min(bbox.x0, c.bbox.x0), y0=min(bbox.y0, c.bbox.y0), x1=max(bbox.x1, c.bbox.x1), y1=max(bbox.y1, c.bbox.y1) ) elif c.bbox: bbox = c.bbox merged_cells.append(TableCell( row=row, col=new_col, row_span=content_cell.row_span, col_span=new_col_span, content=merged_content, bbox=bbox )) processed.add((row, new_col)) logger.info(f"Cell count: {len(cells)} -> {len(merged_cells)}") return merged_cells, new_widths, new_num_cols def _detect_visual_column_boundaries( self, page: fitz.Page, table_bbox: Tuple[float, float, float, float], pymupdf_widths: List[float] ) -> Optional[List[float]]: """ Detect actual column boundaries from page drawings (rectangles). For tables with complex merged cells, PyMuPDF's column detection often creates too many columns. This method analyzes the visual rectangles (cell backgrounds) to find the MAIN column boundaries by frequency analysis. Strategy: 1. Collect all cell rectangles from drawings 2. Count how frequently each x boundary appears (rounded to 5pt) 3. Keep only boundaries that appear frequently (>= threshold) 4. These are the main column boundaries that span most rows Args: page: PyMuPDF page object table_bbox: Table bounding box (x0, y0, x1, y1) pymupdf_widths: Column widths from PyMuPDF detection Returns: List of column boundary x-coordinates, or None if detection fails """ try: from collections import Counter # Collect cell rectangles from page drawings cell_rects = [] drawings = page.get_drawings() for d in drawings: if d.get('items'): for item in d['items']: if item[0] == 're': # Rectangle rect = item[1] # Filter: within table bounds, large enough to be a cell if (rect.x0 >= table_bbox[0] - 5 and rect.x1 <= table_bbox[2] + 5 and rect.y0 >= table_bbox[1] - 5 and rect.y1 <= table_bbox[3] + 5): width = rect.x1 - rect.x0 height = rect.y1 - rect.y0 if width > 30 and height > 15: cell_rects.append(rect) if len(cell_rects) < 4: # Not enough cell rectangles detected logger.debug(f"Only {len(cell_rects)} cell rectangles found, skipping visual detection") return None logger.debug(f"Found {len(cell_rects)} cell rectangles for visual column detection") # Count frequency of each boundary (rounded to 5pt) boundary_counts = Counter() for r in cell_rects: boundary_counts[round(r.x0 / 5) * 5] += 1 boundary_counts[round(r.x1 / 5) * 5] += 1 # Keep only boundaries that appear frequently # Use 8% threshold to catch internal column boundaries (like nested sub-columns) min_frequency = max(3, len(cell_rects) * 0.08) frequent_boundaries = sorted([ x for x, count in boundary_counts.items() if count >= min_frequency ]) # Always include table edges table_left = round(table_bbox[0] / 5) * 5 table_right = round(table_bbox[2] / 5) * 5 if not frequent_boundaries or frequent_boundaries[0] > table_left + 10: frequent_boundaries.insert(0, table_left) if not frequent_boundaries or frequent_boundaries[-1] < table_right - 10: frequent_boundaries.append(table_right) logger.debug(f"Frequent boundaries (min_freq={min_frequency:.0f}): {frequent_boundaries}") if len(frequent_boundaries) < 3: # Need at least 3 boundaries for 2 columns return None # Merge close boundaries (within 10pt) - take the one with higher frequency def merge_close_by_frequency(boundaries, counts, threshold=10): if not boundaries: return [] result = [boundaries[0]] for b in boundaries[1:]: if b - result[-1] <= threshold: # Keep the one with higher frequency if counts[b] > counts[result[-1]]: result[-1] = b else: result.append(b) return result merged_boundaries = merge_close_by_frequency( frequent_boundaries, boundary_counts, threshold=10 ) if len(merged_boundaries) < 3: return None # Calculate column widths widths = [merged_boundaries[i+1] - merged_boundaries[i] for i in range(len(merged_boundaries)-1)] logger.info(f"Visual column detection: {len(widths)} columns") logger.info(f" Boundaries: {merged_boundaries}") logger.info(f" Widths: {[round(w) for w in widths]}") return merged_boundaries except Exception as e: logger.warning(f"Visual column detection failed: {e}") import traceback logger.debug(traceback.format_exc()) return None def _detect_visual_row_boundaries( self, page: fitz.Page, table_bbox: Tuple[float, float, float, float], table_cells: Optional[List] = None ) -> Optional[List[float]]: """ Detect actual row boundaries from table cell bboxes. Uses cell bboxes from PyMuPDF table detection for more accurate row boundary detection than page drawings. Args: page: PyMuPDF page object table_bbox: Table bounding box (x0, y0, x1, y1) table_cells: List of cell bboxes from table.cells (preferred) Returns: List of row boundary y-coordinates, or None if detection fails """ try: from collections import Counter boundary_counts = Counter() cell_count = 0 if table_cells: # Use table cells directly (more accurate for row detection) for cell_bbox in table_cells: if cell_bbox: y0 = round(cell_bbox[1] / 5) * 5 y1 = round(cell_bbox[3] / 5) * 5 boundary_counts[y0] += 1 boundary_counts[y1] += 1 cell_count += 1 else: # Fallback to page drawings drawings = page.get_drawings() for d in drawings: if d.get('items'): for item in d['items']: if item[0] == 're': rect = item[1] if (rect.x0 >= table_bbox[0] - 5 and rect.x1 <= table_bbox[2] + 5 and rect.y0 >= table_bbox[1] - 5 and rect.y1 <= table_bbox[3] + 5): width = rect.x1 - rect.x0 height = rect.y1 - rect.y0 if width > 30 and height > 15: y0 = round(rect.y0 / 5) * 5 y1 = round(rect.y1 / 5) * 5 boundary_counts[y0] += 1 boundary_counts[y1] += 1 cell_count += 1 if cell_count < 4: logger.debug(f"Only {cell_count} cells found, skipping visual row detection") return None # Keep only boundaries that appear frequently # Use 8% threshold similar to column detection min_frequency = max(3, cell_count * 0.08) frequent_boundaries = sorted([ y for y, count in boundary_counts.items() if count >= min_frequency ]) # Always include table edges table_top = round(table_bbox[1] / 5) * 5 table_bottom = round(table_bbox[3] / 5) * 5 if not frequent_boundaries or frequent_boundaries[0] > table_top + 10: frequent_boundaries.insert(0, table_top) if not frequent_boundaries or frequent_boundaries[-1] < table_bottom - 10: frequent_boundaries.append(table_bottom) logger.debug(f"Frequent Y boundaries (min_freq={min_frequency:.0f}): {frequent_boundaries}") if len(frequent_boundaries) < 3: # Need at least 3 boundaries for 2 rows return None # Merge close boundaries (within 10pt) - take the one with higher frequency def merge_close_by_frequency(boundaries, counts, threshold=10): if not boundaries: return [] result = [boundaries[0]] for b in boundaries[1:]: if b - result[-1] <= threshold: # Keep the one with higher frequency if counts[b] > counts[result[-1]]: result[-1] = b else: result.append(b) return result merged_boundaries = merge_close_by_frequency( frequent_boundaries, boundary_counts, threshold=10 ) if len(merged_boundaries) < 3: return None # Calculate row heights heights = [merged_boundaries[i+1] - merged_boundaries[i] for i in range(len(merged_boundaries)-1)] logger.info(f"Visual row detection: {len(heights)} rows") logger.info(f" Y Boundaries: {merged_boundaries}") logger.info(f" Heights: {[round(h) for h in heights]}") return merged_boundaries except Exception as e: logger.warning(f"Visual row detection failed: {e}") import traceback logger.debug(traceback.format_exc()) return None def _remap_cells_to_visual_columns( self, cells: List[TableCell], column_widths: List[float], num_rows: int, num_cols: int, visual_boundaries: List[float], row_boundaries: Optional[List[float]] = None ) -> Tuple[List[TableCell], List[float], int, int]: """ Remap cells from PyMuPDF columns to visual columns based on cell bbox. Args: cells: List of TableCell objects from PyMuPDF column_widths: Original column widths from PyMuPDF num_rows: Number of rows num_cols: Original number of columns visual_boundaries: Column boundaries from visual detection row_boundaries: Row boundaries from visual detection (optional) Returns: Tuple of (remapped_cells, new_widths, new_num_cols, new_num_rows) """ try: new_num_cols = len(visual_boundaries) - 1 new_widths = [visual_boundaries[i+1] - visual_boundaries[i] for i in range(new_num_cols)] new_num_rows = len(row_boundaries) - 1 if row_boundaries else num_rows logger.info(f"Remapping {len(cells)} cells from {num_cols} to {new_num_cols} visual columns") if row_boundaries: logger.info(f"Using {new_num_rows} visual rows for row_span calculation") # Map each cell to visual column and row based on its bbox # This ensures spanning cells are placed at their correct position cell_map = {} # (visual_row, start_col) -> list of cells for cell in cells: if not cell.bbox: continue # Find start column based on left edge of cell cell_x0 = cell.bbox.x0 start_col = 0 # First check if cell_x0 is very close to any boundary (within 5pt) # If so, it belongs to the column that starts at that boundary snapped = False for i in range(1, len(visual_boundaries)): # Skip first (left edge) if abs(cell_x0 - visual_boundaries[i]) <= 5: start_col = min(i, new_num_cols - 1) snapped = True break # If not snapped to boundary, use standard containment check if not snapped: for i in range(new_num_cols): if visual_boundaries[i] <= cell_x0 < visual_boundaries[i+1]: start_col = i break elif cell_x0 >= visual_boundaries[-1]: start_col = new_num_cols - 1 # Find visual row based on top edge of cell visual_row = cell.row # Default to original row if row_boundaries: cell_y0 = cell.bbox.y0 for i in range(new_num_rows): if row_boundaries[i] <= cell_y0 + 5 < row_boundaries[i+1]: visual_row = i break elif cell_y0 >= row_boundaries[-1] - 5: visual_row = new_num_rows - 1 key = (visual_row, start_col) if key not in cell_map: cell_map[key] = [] cell_map[key].append(cell) # Create remapped cells remapped_cells = [] processed = set() for (visual_row, start_col), cell_list in sorted(cell_map.items()): if (visual_row, start_col) in processed: continue # Sort by original column cell_list.sort(key=lambda c: c.col) # Merge content from all cells at this position contents = [] for c in cell_list: if c.content and c.content.strip(): contents.append(c.content.strip()) merged_content = '\n'.join(contents) if contents else '' # Use the cell with tallest bbox for row span calculation # (handles case where multiple cells merge into one) tallest_cell = max(cell_list, key=lambda c: (c.bbox.y1 - c.bbox.y0) if c.bbox else 0) widest_cell = max(cell_list, key=lambda c: (c.bbox.x1 - c.bbox.x0) if c.bbox else 0) # Calculate col_span based on right edge of widest cell col_span = 1 if widest_cell.bbox: cell_x1 = widest_cell.bbox.x1 end_col = start_col for i in range(start_col, new_num_cols): if cell_x1 > visual_boundaries[i] + 5: # 5pt tolerance end_col = i col_span = max(1, end_col - start_col + 1) # Calculate row_span based on visual row boundaries row_span = 1 if row_boundaries and tallest_cell.bbox: cell_y1 = tallest_cell.bbox.y1 # Find end row based on bottom edge of tallest cell end_row = visual_row for i in range(visual_row, new_num_rows): if cell_y1 > row_boundaries[i] + 5: # 5pt tolerance end_row = i row_span = max(1, end_row - visual_row + 1) # Merge bbox from all cells merged_bbox = tallest_cell.bbox for c in cell_list: if c.bbox and merged_bbox: merged_bbox = BoundingBox( x0=min(merged_bbox.x0, c.bbox.x0), y0=min(merged_bbox.y0, c.bbox.y0), x1=max(merged_bbox.x1, c.bbox.x1), y1=max(merged_bbox.y1, c.bbox.y1) ) elif c.bbox: merged_bbox = c.bbox remapped_cells.append(TableCell( row=visual_row, col=start_col, row_span=row_span, col_span=col_span, content=merged_content, bbox=merged_bbox )) processed.add((visual_row, start_col)) # Filter out cells that are covered by spans from other cells # Build a set of positions covered by spans covered_positions = set() for cell in remapped_cells: if cell.col_span > 1 or cell.row_span > 1: for r in range(cell.row, cell.row + cell.row_span): for c in range(cell.col, cell.col + cell.col_span): if (r, c) != (cell.row, cell.col): # Don't cover the origin covered_positions.add((r, c)) # Remove covered cells final_cells = [ cell for cell in remapped_cells if (cell.row, cell.col) not in covered_positions ] logger.info(f"Remapped to {len(final_cells)} cells in {new_num_cols} columns x {new_num_rows} rows (filtered {len(remapped_cells) - len(final_cells)} covered cells)") return final_cells, new_widths, new_num_cols, new_num_rows except Exception as e: logger.error(f"Cell remapping failed: {e}") # Fallback to original return cells, column_widths, num_cols, num_rows def _detect_tables_by_position(self, page: fitz.Page, page_num: int, counter: int) -> List[DocumentElement]: """Detect tables by analyzing text positioning""" tables = [] # Get all words with positions words = page.get_text("words") # Returns (x0, y0, x1, y1, "word", block_no, line_no, word_no) if not words: return tables # Group words by approximate row (y-coordinate) rows = {} for word in words: y = round(word[1] / 5) * 5 # Round to nearest 5 points if y not in rows: rows[y] = [] rows[y].append({ 'x0': word[0], 'y0': word[1], 'x1': word[2], 'y1': word[3], 'text': word[4], 'block': word[5] if len(word) > 5 else 0 }) # Sort rows by y-coordinate sorted_rows = sorted(rows.items(), key=lambda x: x[0]) # Find potential tables (consecutive rows with multiple columns) current_table_rows = [] tables_found = [] for y, words_in_row in sorted_rows: words_in_row.sort(key=lambda w: w['x0']) if len(words_in_row) >= self.min_table_cols: # Check if this could be a table row x_positions = [w['x0'] for w in words_in_row] # Check for somewhat regular spacing if self._has_regular_spacing(x_positions): current_table_rows.append((y, words_in_row)) else: # End current table if exists if len(current_table_rows) >= self.min_table_rows: tables_found.append(current_table_rows) current_table_rows = [] else: # End current table if exists if len(current_table_rows) >= self.min_table_rows: tables_found.append(current_table_rows) current_table_rows = [] # Don't forget the last table if len(current_table_rows) >= self.min_table_rows: tables_found.append(current_table_rows) # Convert detected tables to DocumentElements for table_idx, table_rows in enumerate(tables_found): if not table_rows: continue # Calculate table bounding box all_words = [] for _, words in table_rows: all_words.extend(words) min_x = min(w['x0'] for w in all_words) min_y = min(w['y0'] for w in all_words) max_x = max(w['x1'] for w in all_words) max_y = max(w['y1'] for w in all_words) bbox = BoundingBox(x0=min_x, y0=min_y, x1=max_x, y1=max_y) # Create table cells cells = [] for row_idx, (y, words) in enumerate(table_rows): # Group words into columns columns = self._group_into_columns(words, table_rows) for col_idx, col_text in enumerate(columns): if col_text: cells.append(TableCell( row=row_idx, col=col_idx, content=col_text )) # Create table data table_data = TableData( rows=len(table_rows), cols=max(len(self._group_into_columns(words, table_rows)) for _, words in table_rows), cells=cells ) element = DocumentElement( element_id=f"table_{page_num}_{counter + table_idx}", type=ElementType.TABLE, content=table_data, bbox=bbox, confidence=0.8, # Lower confidence for positional detection metadata={"detection_method": "positional"} ) tables.append(element) return tables def _has_regular_spacing(self, x_positions: List[float], tolerance: float = 0.3) -> bool: """Check if x positions have somewhat regular spacing""" if len(x_positions) < 3: return False spacings = [x_positions[i+1] - x_positions[i] for i in range(len(x_positions)-1)] avg_spacing = sum(spacings) / len(spacings) # Check if spacings are within tolerance of average for spacing in spacings: if abs(spacing - avg_spacing) > avg_spacing * tolerance: return False return True def _group_into_columns(self, words: List[Dict], all_rows: List) -> List[str]: """Group words into columns based on x-position""" if not words: return [] # Find common column positions across all rows all_x_positions = [] for _, row_words in all_rows: all_x_positions.extend([w['x0'] for w in row_words]) # Cluster x-positions to find columns column_positions = self._cluster_positions(all_x_positions) # Assign words to columns columns = [""] * len(column_positions) for word in words: # Find closest column closest_col = 0 min_dist = float('inf') for col_idx, col_x in enumerate(column_positions): dist = abs(word['x0'] - col_x) if dist < min_dist: min_dist = dist closest_col = col_idx if columns[closest_col]: columns[closest_col] += " " + word['text'] else: columns[closest_col] = word['text'] return columns def _cluster_positions(self, positions: List[float], threshold: float = 20) -> List[float]: """Cluster positions to find common columns""" if not positions: return [] sorted_pos = sorted(positions) clusters = [[sorted_pos[0]]] for pos in sorted_pos[1:]: # Check if position belongs to current cluster if pos - clusters[-1][-1] < threshold: clusters[-1].append(pos) else: clusters.append([pos]) # Return average position of each cluster return [sum(cluster) / len(cluster) for cluster in clusters] def _extract_images(self, page: fitz.Page, page_num: int, document_id: str, counter: int, output_dir: Optional[Path], covering_images: Optional[List[Dict]] = None, covered_bboxes: Optional[List[fitz.Rect]] = None) -> List[DocumentElement]: """ Extract images from page, filtering out tiny decoration images and covering images. Filtering applied: 1. Images smaller than min_image_area (default 200 px²) are filtered as decorations 2. Images that match detected covering images (redaction rectangles) are filtered 3. Dark images that overlap significantly with covering vector rectangles are filtered """ elements = [] image_list = page.get_images() filtered_tiny = 0 filtered_covering = 0 covering_images = covering_images or [] covered_bboxes = covered_bboxes or [] # Build covering image xrefs for quick lookup covering_xrefs = set() for cov in covering_images: if 'xref' in cov: covering_xrefs.add(cov['xref']) for img_idx, img in enumerate(image_list): try: xref = img[0] # Filter out covering images (redaction rectangles) if xref in covering_xrefs: filtered_covering += 1 logger.debug(f"Filtering covering image {img_idx} (xref={xref})") continue # Get image position(s) img_rects = page.get_image_rects(xref) if not img_rects: continue rect = img_rects[0] # Use first occurrence # Calculate image area and filter tiny decoration images image_area = (rect.x1 - rect.x0) * (rect.y1 - rect.y0) if self.min_image_area > 0 and image_area < self.min_image_area: filtered_tiny += 1 logger.debug( f"Filtering tiny image {img_idx}: area={image_area:.1f} px² " f"< threshold={self.min_image_area} px²" ) continue # Check for IoU overlap with covering images (for cases without matching xref) is_covering = False if covering_images: for cov in covering_images: cov_bbox = cov.get('bbox', []) if len(cov_bbox) >= 4: iou = self._calculate_iou( [rect.x0, rect.y0, rect.x1, rect.y1], cov_bbox ) if iou > 0.8: # High overlap indicates same image is_covering = True filtered_covering += 1 logger.debug( f"Filtering covering image {img_idx} by IoU={iou:.2f}" ) break if is_covering: continue # Check if dark image overlaps with covering vector rectangles # This catches cases where dark images are placed on top of covering rectangles if covered_bboxes: img_rect = fitz.Rect(rect) for cov_rect in covered_bboxes: intersection = img_rect & cov_rect if not intersection.is_empty: img_area = img_rect.width * img_rect.height if img_area > 0: overlap_ratio = (intersection.width * intersection.height) / img_area # If significant overlap (>50%), check if image is dark if overlap_ratio > 0.5: # Analyze image darkness try: from PIL import Image import io base_image = page.parent.extract_image(xref) img_bytes = base_image.get('image') if img_bytes: pil_img = Image.open(io.BytesIO(img_bytes)) if pil_img.mode != 'RGB': pil_img = pil_img.convert('RGB') img_small = pil_img.resize((min(30, pil_img.width), min(30, pil_img.height))) pixels = list(img_small.getdata()) if pixels: avg_r = sum(p[0] for p in pixels) / len(pixels) avg_g = sum(p[1] for p in pixels) / len(pixels) avg_b = sum(p[2] for p in pixels) / len(pixels) max_channel = max(avg_r, avg_g, avg_b) # Filter dark images (max channel <= 60) if max_channel <= 60: filtered_covering += 1 logger.debug( f"Filtering dark image {img_idx} overlapping with covering rect " f"(overlap={overlap_ratio:.1%}, max_channel={max_channel:.1f})" ) is_covering = True break except Exception as e: logger.debug(f"Failed to analyze image darkness: {e}") if is_covering: continue bbox = BoundingBox( x0=rect.x0, y0=rect.y0, x1=rect.x1, y1=rect.y1 ) # Extract image data pix = fitz.Pixmap(page.parent, xref) image_data = { "width": pix.width, "height": pix.height, "colorspace": pix.colorspace.name if pix.colorspace else "unknown", "xref": xref, "area": image_area } # Save image if output directory provided if output_dir: output_dir.mkdir(parents=True, exist_ok=True) image_filename = f"{document_id}_p{page_num}_img{img_idx}.png" image_path = output_dir / image_filename pix.save(str(image_path)) # Store relative filename only (consistent with OCR track) # PDF generator will join with result_dir to get full path image_data["saved_path"] = image_filename logger.debug(f"Saved image to {image_path}") element = DocumentElement( element_id=f"image_{page_num}_{counter + img_idx}", type=ElementType.IMAGE, content=image_data, bbox=bbox, confidence=1.0, metadata={ "image_index": img_idx, "xref": xref } ) elements.append(element) pix = None # Free memory except Exception as e: logger.error(f"Error extracting image {img_idx}: {e}") if filtered_tiny > 0 or filtered_covering > 0: logger.info( f"Page {page_num}: Filtered images - " f"{filtered_tiny} tiny (< {self.min_image_area} px²), " f"{filtered_covering} covering/redaction" ) return elements def has_missing_images(self, page: fitz.Page) -> bool: """ Detect if a page likely has images that weren't extracted. This checks for inline image blocks (type=1 in text dict) which indicate graphics composed of many small image blocks (like logos) that page.get_images() cannot detect. Args: page: PyMuPDF page object Returns: True if there are likely missing images that need OCR extraction """ try: # Check if get_images found anything standard_images = page.get_images() if standard_images: return False # Standard images were found, no need for fallback # Check for inline image blocks (type=1) text_dict = page.get_text("dict", sort=True) blocks = text_dict.get("blocks", []) image_block_count = sum(1 for b in blocks if b.get("type") == 1) # If there are many inline image blocks, likely there's a logo or graphic if image_block_count >= 10: logger.info(f"Detected {image_block_count} inline image blocks - may need OCR for image extraction") return True return False except Exception as e: logger.warning(f"Error checking for missing images: {e}") return False def check_document_for_missing_images(self, pdf_path: Path) -> List[int]: """ Check a PDF document for pages that likely have missing images. This opens the PDF and checks each page for inline image blocks that weren't extracted by get_images(). Args: pdf_path: Path to the PDF file Returns: List of page numbers (1-indexed) that have missing images """ pages_with_missing_images = [] try: doc = fitz.open(str(pdf_path)) for page_num in range(len(doc)): page = doc[page_num] if self.has_missing_images(page): pages_with_missing_images.append(page_num + 1) # 1-indexed doc.close() if pages_with_missing_images: logger.info(f"Document has missing images on pages: {pages_with_missing_images}") except Exception as e: logger.error(f"Error checking document for missing images: {e}") return pages_with_missing_images def render_inline_image_regions( self, pdf_path: Path, unified_doc: 'UnifiedDocument', pages: List[int], output_dir: Optional[Path] = None ) -> int: """ Render inline image regions and add them to the unified document. This is a fallback when OCR doesn't detect images. It clusters inline image blocks (type=1) and renders them as images. Args: pdf_path: Path to the PDF file unified_doc: UnifiedDocument to add images to pages: List of page numbers (1-indexed) to process output_dir: Directory to save rendered images Returns: Number of images added """ images_added = 0 try: doc = fitz.open(str(pdf_path)) for page_num in pages: if page_num < 1 or page_num > len(doc): continue page = doc[page_num - 1] # 0-indexed page_rect = page.rect # Get inline image blocks text_dict = page.get_text("dict", sort=True) blocks = text_dict.get("blocks", []) image_blocks = [] for block in blocks: if block.get("type") == 1: # Image block bbox = block.get("bbox") if bbox: image_blocks.append(fitz.Rect(bbox)) if len(image_blocks) < 5: # Reduced from 10 logger.debug(f"Page {page_num}: Only {len(image_blocks)} inline image blocks, skipping") continue logger.info(f"Page {page_num}: Found {len(image_blocks)} inline image blocks") # Cluster nearby image blocks regions = self._cluster_nearby_rects(image_blocks, tolerance=5.0) logger.info(f"Page {page_num}: Clustered into {len(regions)} regions") # Find the corresponding page in unified_doc target_page = None for p in unified_doc.pages: if p.page_number == page_num: target_page = p break if not target_page: continue for region_idx, region_rect in enumerate(regions): logger.info(f"Page {page_num} region {region_idx}: {region_rect} (w={region_rect.width:.1f}, h={region_rect.height:.1f})") # Skip very small regions if region_rect.width < 30 or region_rect.height < 30: logger.info(f" -> Skipped: too small (min 30x30)") continue # Skip regions that are primarily in the table area (below top 40%) # But allow regions that START in the top portion page_30_pct = page_rect.height * 0.3 page_40_pct = page_rect.height * 0.4 if region_rect.y0 > page_40_pct: logger.info(f" -> Skipped: y0={region_rect.y0:.1f} > 40% of page ({page_40_pct:.1f})") continue logger.info(f"Rendering inline image region {region_idx} on page {page_num}: {region_rect}") try: # Add small padding clip_rect = region_rect + (-2, -2, 2, 2) clip_rect.intersect(page_rect) # Render at 2x resolution mat = fitz.Matrix(2, 2) pix = page.get_pixmap(clip=clip_rect, matrix=mat, alpha=False) # Create bounding box bbox = BoundingBox( x0=clip_rect.x0, y0=clip_rect.y0, x1=clip_rect.x1, y1=clip_rect.y1 ) image_data = { "width": pix.width, "height": pix.height, "colorspace": "rgb", "type": "inline_region" } # Save image if output directory provided if output_dir: output_dir.mkdir(parents=True, exist_ok=True) doc_id = unified_doc.document_id or "unknown" image_filename = f"{doc_id}_p{page_num}_logo{region_idx}.png" image_path = output_dir / image_filename pix.save(str(image_path)) image_data["saved_path"] = image_filename logger.info(f"Saved inline image region to {image_path}") element = DocumentElement( element_id=f"logo_{page_num}_{region_idx}", type=ElementType.LOGO, content=image_data, bbox=bbox, confidence=0.9, metadata={ "region_type": "inline_image_blocks", "block_count": len(image_blocks) } ) target_page.elements.append(element) images_added += 1 pix = None # Free memory except Exception as e: logger.error(f"Error rendering inline image region {region_idx}: {e}") doc.close() if images_added > 0: logger.info(f"Added {images_added} inline image regions to document") except Exception as e: logger.error(f"Error rendering inline image regions: {e}") return images_added def _cluster_nearby_rects(self, rects: List[fitz.Rect], tolerance: float = 5.0) -> List[fitz.Rect]: """Cluster nearby rectangles into regions.""" if not rects: return [] sorted_rects = sorted(rects, key=lambda r: (r.y0, r.x0)) merged = [] for rect in sorted_rects: merged_with_existing = False for i, region in enumerate(merged): expanded = region + (-tolerance, -tolerance, tolerance, tolerance) if expanded.intersects(rect): merged[i] = region | rect merged_with_existing = True break if not merged_with_existing: merged.append(rect) # Second pass: merge any regions that now overlap changed = True while changed: changed = False new_merged = [] skip = set() for i, r1 in enumerate(merged): if i in skip: continue current = r1 for j, r2 in enumerate(merged[i+1:], start=i+1): if j in skip: continue expanded = current + (-tolerance, -tolerance, tolerance, tolerance) if expanded.intersects(r2): current = current | r2 skip.add(j) changed = True new_merged.append(current) merged = new_merged return merged def _extract_vector_graphics(self, page: fitz.Page, page_num: int, document_id: str, counter: int, output_dir: Optional[Path], table_bboxes: Optional[List[BoundingBox]] = None) -> List[DocumentElement]: """ Extract vector graphics (charts, diagrams) from page. This method identifies regions that are composed of vector drawing commands (paths, lines, rectangles) rather than embedded raster images. These are typically charts created in Excel, vector diagrams, or other graphics. Args: page: PyMuPDF page object page_num: Page number (1-indexed) document_id: Unique document identifier counter: Starting counter for element IDs output_dir: Directory to save rendered graphics table_bboxes: List of table bounding boxes to exclude table border drawings Returns: List of DocumentElement objects representing vector graphics """ elements = [] try: # Get all drawing commands drawings = page.get_drawings() if not drawings: return elements logger.debug(f"Page {page_num} contains {len(drawings)} vector drawing commands") # Filter out drawings that are likely table borders # Table borders are typically thin rectangular lines within table regions non_table_drawings = self._filter_table_border_drawings(drawings, table_bboxes) logger.debug(f"After filtering table borders: {len(non_table_drawings)} drawings remain") if not non_table_drawings: logger.debug("All drawings appear to be table borders, no vector graphics to extract") return elements # Cluster drawings into groups (charts, diagrams, etc.) try: # Use custom clustering that only considers non-table drawings drawing_clusters = self._cluster_non_table_drawings(page, non_table_drawings) logger.debug(f"Clustered into {len(drawing_clusters)} groups") except (AttributeError, TypeError) as e: # cluster_drawings not available or has different signature # Fallback: try to identify charts by analyzing drawing density logger.warning(f"Custom clustering failed ({e}), using fallback method") drawing_clusters = self._cluster_drawings_fallback(page, non_table_drawings) # Get page dimensions for filtering page_rect = page.rect page_area = page_rect.width * page_rect.height for cluster_idx, bbox in enumerate(drawing_clusters): # Ignore small regions (likely noise or separator lines) if bbox.width < 50 or bbox.height < 50: logger.debug(f"Skipping small cluster {cluster_idx}: {bbox.width:.1f}x{bbox.height:.1f}") continue # Ignore very large regions that cover most of the page # These are usually background elements, page borders, or misdetected regions cluster_area = bbox.width * bbox.height if cluster_area > page_area * 0.7: # More than 70% of page logger.debug(f"Skipping large cluster {cluster_idx}: covers {cluster_area/page_area*100:.0f}% of page") continue # Render the region to a raster image # matrix=fitz.Matrix(2, 2) increases resolution to ~200 DPI try: pix = page.get_pixmap(clip=bbox, matrix=fitz.Matrix(2, 2)) # Save image if output directory provided if output_dir: output_dir.mkdir(parents=True, exist_ok=True) filename = f"{document_id}_p{page_num}_chart{cluster_idx}.png" filepath = output_dir / filename pix.save(str(filepath)) # Create DocumentElement image_data = { "saved_path": str(filepath), "width": pix.width, "height": pix.height, "colorspace": pix.colorspace.name if pix.colorspace else "unknown", "source": "vector_graphics" } element = DocumentElement( element_id=f"chart_{page_num}_{counter + cluster_idx}", type=ElementType.CHART, # Use CHART type for vector graphics content=image_data, bbox=BoundingBox( x0=bbox.x0, y0=bbox.y0, x1=bbox.x1, y1=bbox.y1 ), confidence=0.85, # Slightly lower confidence than raster images metadata={ "cluster_index": cluster_idx, "drawing_count": len(drawings) } ) elements.append(element) logger.debug(f"Extracted chart {cluster_idx}: {bbox.width:.1f}x{bbox.height:.1f} -> {filepath}") pix = None # Free memory except Exception as e: logger.error(f"Error rendering vector graphic cluster {cluster_idx}: {e}") continue except Exception as e: logger.error(f"Error extracting vector graphics: {e}") return elements def _cluster_drawings_fallback(self, page: fitz.Page, drawings: list) -> list: """ Fallback method to cluster drawings when cluster_drawings() is not available. This uses a simple spatial clustering approach based on bounding boxes. """ if not drawings: return [] # Collect all drawing bounding boxes bboxes = [] for drawing in drawings: rect = drawing.get('rect') if rect: bboxes.append(fitz.Rect(rect)) if not bboxes: return [] # Simple clustering: merge overlapping or nearby rectangles clusters = [] tolerance = 20 for bbox in bboxes: # Try to merge with existing cluster merged = False for i, cluster in enumerate(clusters): # Check if bbox is close to this cluster expanded_cluster = cluster + (-tolerance, -tolerance, tolerance, tolerance) if expanded_cluster.intersects(bbox): # Merge bbox into cluster clusters[i] = cluster | bbox # Union of rectangles merged = True break if not merged: # Create new cluster clusters.append(bbox) # Filter out very small clusters filtered_clusters = [c for c in clusters if c.width >= 50 and c.height >= 50] logger.debug(f"Fallback clustering: {len(bboxes)} drawings -> {len(clusters)} clusters -> {len(filtered_clusters)} filtered") return filtered_clusters def _filter_table_border_drawings(self, drawings: list, table_bboxes: Optional[List[BoundingBox]]) -> list: """ Filter out drawings that are likely table borders. Table borders are typically: - Thin rectangular lines (height or width < 5pt) - Located within or on the edge of table bounding boxes Args: drawings: List of PyMuPDF drawing objects table_bboxes: List of table bounding boxes Returns: List of drawings that are NOT table borders (likely logos, charts, etc.) """ if not table_bboxes: return drawings non_table_drawings = [] table_border_count = 0 for drawing in drawings: rect = drawing.get('rect') if not rect: continue draw_rect = fitz.Rect(rect) # Check if this drawing is a thin line (potential table border) is_thin_line = draw_rect.width < 5 or draw_rect.height < 5 # Check if drawing overlaps significantly with any table overlaps_table = False for table_bbox in table_bboxes: table_rect = fitz.Rect(table_bbox.x0, table_bbox.y0, table_bbox.x1, table_bbox.y1) # Expand table rect slightly to include border lines on edges expanded_table = table_rect + (-5, -5, 5, 5) if expanded_table.contains(draw_rect) or expanded_table.intersects(draw_rect): # Calculate overlap ratio intersection = draw_rect & expanded_table if not intersection.is_empty: overlap_ratio = intersection.get_area() / draw_rect.get_area() if draw_rect.get_area() > 0 else 0 # If drawing is mostly inside table region, it's likely a border if overlap_ratio > 0.8: overlaps_table = True break # Keep drawing if it's NOT (thin line AND overlapping table) # This keeps: logos (complex shapes), charts outside tables, etc. if is_thin_line and overlaps_table: table_border_count += 1 else: non_table_drawings.append(drawing) if table_border_count > 0: logger.debug(f"Filtered out {table_border_count} table border drawings") return non_table_drawings def _cluster_non_table_drawings(self, page: fitz.Page, drawings: list) -> list: """ Cluster non-table drawings into groups. This method clusters drawings that have been pre-filtered to exclude table borders. It uses a more conservative clustering approach suitable for logos and charts. Args: page: PyMuPDF page object drawings: Pre-filtered list of drawings (excluding table borders) Returns: List of fitz.Rect representing clustered drawing regions """ if not drawings: return [] # Collect all drawing bounding boxes bboxes = [] for drawing in drawings: rect = drawing.get('rect') if rect: bboxes.append(fitz.Rect(rect)) if not bboxes: return [] # More conservative clustering with smaller tolerance # This prevents grouping distant graphics together clusters = [] tolerance = 10 # Smaller tolerance than fallback (was 20) for bbox in bboxes: # Try to merge with existing cluster merged = False for i, cluster in enumerate(clusters): # Check if bbox is close to this cluster expanded_cluster = cluster + (-tolerance, -tolerance, tolerance, tolerance) if expanded_cluster.intersects(bbox): # Merge bbox into cluster clusters[i] = cluster | bbox # Union of rectangles merged = True break if not merged: # Create new cluster clusters.append(bbox) # Filter out very small clusters (noise) # Keep minimum 30x30 for logos (smaller than default 50x50) filtered_clusters = [c for c in clusters if c.width >= 30 and c.height >= 30] logger.debug(f"Non-table clustering: {len(bboxes)} drawings -> {len(clusters)} clusters -> {len(filtered_clusters)} filtered") return filtered_clusters def _deduplicate_table_chart_overlap(self, elements: List[DocumentElement]) -> List[DocumentElement]: """ Intelligently resolve TABLE-CHART overlaps based on table structure completeness. When a region is detected as both TABLE and CHART: - Calculate cell completeness = actual_cells / (rows × cols) - If completeness ≥50% → Real table with complete structure → Keep TABLE - If completeness <50% → False positive (chart detected as table) → Keep CHART Args: elements: List of extracted elements Returns: Filtered list with low-quality overlaps removed """ # Collect all tables and charts tables = [elem for elem in elements if elem.type == ElementType.TABLE] charts = [elem for elem in elements if elem.type == ElementType.CHART] if not tables or not charts: return elements # No potential conflicts # Analyze TABLE structure completeness # For tables with merged cells, completeness = positions covered / total positions table_completeness = {} for table in tables: if hasattr(table.content, 'rows') and hasattr(table.content, 'cols') and hasattr(table.content, 'cells'): expected_positions = table.content.rows * table.content.cols # Calculate actual coverage accounting for merged cells # Each cell covers row_span × col_span positions covered_positions = 0 for cell in table.content.cells: row_span = getattr(cell, 'row_span', 1) or 1 col_span = getattr(cell, 'col_span', 1) or 1 covered_positions += row_span * col_span if expected_positions > 0: completeness = covered_positions / expected_positions table_completeness[table.element_id] = completeness else: table_completeness[table.element_id] = 0.0 else: table_completeness[table.element_id] = 0.0 # Check overlaps and decide what to keep filtered_elements = [] removed_charts = 0 removed_tables = 0 # Process TABLEs for table in tables: if not table.bbox: filtered_elements.append(table) continue # Check if this TABLE overlaps with any CHART overlaps_chart = False for chart in charts: if not chart.bbox: continue # Calculate overlap overlap_x0 = max(table.bbox.x0, chart.bbox.x0) overlap_y0 = max(table.bbox.y0, chart.bbox.y0) overlap_x1 = min(table.bbox.x1, chart.bbox.x1) overlap_y1 = min(table.bbox.y1, chart.bbox.y1) if overlap_x0 < overlap_x1 and overlap_y0 < overlap_y1: overlap_area = (overlap_x1 - overlap_x0) * (overlap_y1 - overlap_y0) table_area = (table.bbox.x1 - table.bbox.x0) * (table.bbox.y1 - table.bbox.y0) if table_area > 0: overlap_ratio = overlap_area / table_area if overlap_ratio >= 0.8: overlaps_chart = True completeness = table_completeness.get(table.element_id, 0.0) logger.debug( f"TABLE-CHART overlap: {table.element_id} vs {chart.element_id}: " f"{overlap_ratio*100:.1f}% overlap, TABLE cell completeness: {completeness*100:.1f}%" ) # Decision: Keep TABLE only if structure is complete if completeness < 0.5: # <50% cell completeness logger.info( f"Removing incomplete TABLE {table.element_id} " f"({completeness*100:.1f}% completeness, overlaps with CHART {chart.element_id})" ) removed_tables += 1 break else: logger.info( f"Keeping TABLE {table.element_id} with {completeness*100:.1f}% completeness " f"(will remove overlapping CHART {chart.element_id})" ) if not overlaps_chart or table_completeness.get(table.element_id, 0.0) >= 0.5: filtered_elements.append(table) # Process CHARTs for chart in charts: if not chart.bbox: filtered_elements.append(chart) continue # Check if this CHART should be removed due to overlap with high-quality TABLE should_remove = False for table in tables: if not table.bbox: continue # Calculate overlap overlap_x0 = max(chart.bbox.x0, table.bbox.x0) overlap_y0 = max(chart.bbox.y0, table.bbox.y0) overlap_x1 = min(chart.bbox.x1, table.bbox.x1) overlap_y1 = min(chart.bbox.y1, table.bbox.y1) if overlap_x0 < overlap_x1 and overlap_y0 < overlap_y1: overlap_area = (overlap_x1 - overlap_x0) * (overlap_y1 - overlap_y0) chart_area = (chart.bbox.x1 - chart.bbox.x0) * (chart.bbox.y1 - chart.bbox.y0) if chart_area > 0: overlap_ratio = overlap_area / chart_area if overlap_ratio >= 0.8: completeness = table_completeness.get(table.element_id, 0.0) # Remove CHART only if TABLE structure is complete if completeness >= 0.5: should_remove = True logger.info( f"Removing CHART {chart.element_id} " f"({overlap_ratio*100:.1f}% overlap with TABLE {table.element_id} having {completeness*100:.1f}% completeness)" ) removed_charts += 1 break if not should_remove: filtered_elements.append(chart) # Process all other elements for elem in elements: if elem.type not in [ElementType.TABLE, ElementType.CHART]: filtered_elements.append(elem) if removed_charts > 0 or removed_tables > 0: logger.info( f"Deduplication complete: removed {removed_tables} incomplete TABLE(s), " f"{removed_charts} overlapping CHART(s)" ) return filtered_elements # ========================================================================= # PDF Preprocessing Pipeline Methods # ========================================================================= def _preprocess_page(self, page: fitz.Page, page_num: int, doc: fitz.Document = None) -> Dict[str, Any]: """ Run preprocessing pipeline on a page before extraction. Pipeline steps: 1. Content sanitization (clean_contents) 2. Hidden layer detection (OCG) 3. White-out/black-out detection (vector rectangles) 4. Covering image detection (embedded black/white images) Args: page: PyMuPDF page object page_num: Page number (1-indexed) doc: PyMuPDF document object (needed for image analysis) Returns: Dict with preprocessing results: - covered_word_bboxes: List of bboxes for text covered by rectangles/images - covering_images: List of covering image info - hidden_layers: List of hidden OCG layer names - sanitized: Whether content was sanitized """ result = { 'covered_word_bboxes': [], 'covering_images': [], 'hidden_layers': [], 'sanitized': False } # Step 1.1: Content sanitization if self.enable_content_sanitization: try: page.clean_contents(sanitize=True) result['sanitized'] = True logger.debug(f"Page {page_num}: Content stream sanitized") except Exception as e: logger.warning(f"Page {page_num}: Content sanitization failed: {e}") # Step 1.3: White-out/black-out detection (vector rectangles) if self.enable_whiteout_detection: covered, covering_rect_bboxes = self._detect_whiteout_covered_text(page, page_num) result['covered_word_bboxes'] = [fitz.Rect(w['bbox']) for w in covered] result['covered_words_detail'] = covered # Include color_type info result['covering_rect_bboxes'] = covering_rect_bboxes # Actual covering rectangles if covered: # Count by color type white_covered = sum(1 for c in covered if c.get('color_type') == 'white') black_covered = sum(1 for c in covered if c.get('color_type') == 'black') other_covered = len(covered) - white_covered - black_covered logger.info(f"Page {page_num}: Detected {len(covered)} covered text regions " f"(white: {white_covered}, black/redaction: {black_covered}, other: {other_covered})") # Step 1.4: Covering image detection (embedded black/white images) if self.enable_whiteout_detection and doc is not None: covering_images = self._detect_covering_images(page, doc, page_num) result['covering_images'] = covering_images # Add covering image bboxes to the covered_word_bboxes list for img in covering_images: result['covered_word_bboxes'].append(fitz.Rect(img['bbox'])) if covering_images: black_imgs = sum(1 for c in covering_images if c['color_type'] == 'image_black') white_imgs = sum(1 for c in covering_images if c['color_type'] == 'image_white') logger.info(f"Page {page_num}: Detected {len(covering_images)} covering images " f"(black: {black_imgs}, white: {white_imgs})") return result def _detect_whiteout_covered_text(self, page: fitz.Page, page_num: int) -> Tuple[List[Dict], List[fitz.Rect]]: """ Detect text covered by solid color rectangles (white-out, black redaction, or any solid fill). Uses IoU (Intersection over Union) to determine if text is covered. Args: page: PyMuPDF page object page_num: Page number for logging Returns: Tuple of: - List of dicts with covered text info: {'text', 'bbox', 'coverage', 'color_type'} - List of covering rectangle bboxes (fitz.Rect) """ covered_words = [] covering_rect_bboxes = [] # Return the actual rectangles page_rect = page.rect # Page boundaries # Get all drawings and find solid-filled rectangles drawings = page.get_drawings() covering_rects = [] # List of (rect, color_type) for d in drawings: fill_color = d.get('fill') if fill_color and isinstance(fill_color, (tuple, list)) and len(fill_color) >= 3: r, g, b = fill_color[:3] rect = d.get('rect') if not rect: continue fitz_rect = fitz.Rect(rect) # Skip very small rectangles (likely not covering blocks) if fitz_rect.width < 5 or fitz_rect.height < 5: continue # Skip rectangles completely outside page boundaries if not fitz_rect.intersects(page_rect): continue # Clip rectangle to page boundaries fitz_rect = fitz_rect & page_rect # Detect white rectangles (white-out / correction tape) # Must be pure white (>= 0.98) to avoid false positives from light backgrounds if r >= 0.98 and g >= 0.98 and b >= 0.98: covering_rects.append((fitz_rect, 'white')) # Detect dark rectangles (redaction / censoring) # Includes pure black AND dark gray (threshold 0.3) # Dark gray is commonly used for redaction boxes elif max(r, g, b) <= 0.3: covering_rects.append((fitz_rect, 'black')) if not covering_rects: return covered_words, covering_rect_bboxes # Extract covering rectangle bboxes for image filtering covering_rect_bboxes = [rect for rect, _ in covering_rects] # Log detected covering rectangles by type white_count = sum(1 for _, t in covering_rects if t == 'white') black_count = sum(1 for _, t in covering_rects if t == 'black') logger.debug(f"Page {page_num}: Found {len(covering_rects)} potential covering rectangles " f"(white: {white_count}, black/redaction: {black_count})") # Get all text words with bounding boxes # words format: (x0, y0, x1, y1, word, block_no, line_no, word_no) words = page.get_text("words") for word_info in words: word_rect = fitz.Rect(word_info[:4]) word_text = word_info[4] word_area = word_rect.width * word_rect.height if word_area <= 0: continue for cover_rect, color_type in covering_rects: # Calculate intersection intersection = word_rect & cover_rect if intersection.is_empty: continue intersection_area = intersection.width * intersection.height coverage_ratio = intersection_area / word_area # Check if coverage exceeds IoU threshold if coverage_ratio >= self.whiteout_iou_threshold: covered_words.append({ 'text': word_text, 'bbox': tuple(word_rect), 'coverage': coverage_ratio, 'color_type': color_type }) break # Word is covered, no need to check other rects return covered_words, covering_rect_bboxes def _detect_covering_images(self, page: fitz.Page, doc: fitz.Document, page_num: int) -> List[Dict]: """ Detect embedded images that are mostly black/white AND actually cover text. Only reports images that: 1. Are mostly solid black or white 2. Are within page boundaries 3. Actually overlap with text content (IoU check) Args: page: PyMuPDF page object doc: PyMuPDF document object (needed for image extraction) page_num: Page number for logging Returns: List of dicts with covering image info: {'bbox', 'color_type', 'avg_color', 'covered_text_count'} """ covering_images = [] page_rect = page.rect # Page boundaries try: # Get all images on the page with their positions image_list = page.get_images(full=True) if not image_list: return covering_images # Get all text words for coverage check words = page.get_text("words") # (x0, y0, x1, y1, word, block_no, line_no, word_no) for img_info in image_list: xref = img_info[0] width = img_info[2] height = img_info[3] # Skip very small images (icons, bullets) if width < 20 or height < 10: continue try: # Extract image data base_image = doc.extract_image(xref) img_bytes = base_image.get('image') if not img_bytes: continue # Analyze image color using PIL from PIL import Image import io img = Image.open(io.BytesIO(img_bytes)) if img.mode != 'RGB': img = img.convert('RGB') # Sample pixels for efficiency (don't analyze every pixel) img_small = img.resize((min(50, img.width), min(50, img.height))) pixels = list(img_small.getdata()) if not pixels: continue avg_r = sum(p[0] for p in pixels) / len(pixels) avg_g = sum(p[1] for p in pixels) / len(pixels) avg_b = sum(p[2] for p in pixels) / len(pixels) # Determine if image is mostly black or white # Use max channel value to detect dark images (allows slight color tint) max_channel = max(avg_r, avg_g, avg_b) min_channel = min(avg_r, avg_g, avg_b) color_type = None is_pure_solid = False # Pure black/white should always be filtered if max_channel <= 40: # Dark image (any channel <= 40) color_type = 'image_black' # Check if it's pure solid black (should always filter) if max_channel <= 5: is_pure_solid = True elif min_channel >= 245: # Bright image (any channel >= 245) color_type = 'image_white' if min_channel >= 250: is_pure_solid = True if color_type: # Get image position on page for img_rect in page.get_image_rects(xref): # Skip images completely outside page boundaries if not img_rect.intersects(page_rect): continue # Clip image rect to page boundaries clipped_rect = img_rect & page_rect # Check if image actually covers any text (IoU check) covered_text_count = 0 for word_info in words: word_rect = fitz.Rect(word_info[:4]) word_area = word_rect.width * word_rect.height if word_area <= 0: continue intersection = word_rect & clipped_rect if not intersection.is_empty: intersection_area = intersection.width * intersection.height coverage_ratio = intersection_area / word_area # Count as covered if >= 50% of word is under the image if coverage_ratio >= 0.5: covered_text_count += 1 # Report if image covers text OR is pure solid black/white # Pure solid fills are likely redaction/placeholder boxes if covered_text_count > 0 or is_pure_solid: covering_images.append({ 'xref': xref, # Include xref for filtering 'bbox': tuple(clipped_rect), 'color_type': color_type, 'avg_color': (avg_r, avg_g, avg_b), 'size': (width, height), 'covered_text_count': covered_text_count, 'is_pure_solid': is_pure_solid }) except Exception as e: logger.debug(f"Page {page_num}: Failed to analyze image xref={xref}: {e}") continue if covering_images: black_count = sum(1 for c in covering_images if c['color_type'] == 'image_black') white_count = sum(1 for c in covering_images if c['color_type'] == 'image_white') total_covered = sum(c.get('covered_text_count', 0) for c in covering_images) logger.debug(f"Page {page_num}: Found {len(covering_images)} covering images " f"(black: {black_count}, white: {white_count}, covering {total_covered} text regions)") except Exception as e: logger.warning(f"Page {page_num}: Failed to detect covering images: {e}") return covering_images def _get_hidden_ocg_layers(self, doc: fitz.Document) -> List[str]: """ Get list of hidden Optional Content Group (OCG) layer names. Args: doc: PyMuPDF document object Returns: List of hidden layer names """ hidden_layers = [] try: ocgs = doc.get_ocgs() if not ocgs: return hidden_layers for ocg_xref, ocg_info in ocgs.items(): # Check if layer is hidden by default if ocg_info.get('on') == False: layer_name = ocg_info.get('name', f'OCG_{ocg_xref}') hidden_layers.append(layer_name) logger.debug(f"Found hidden OCG layer: {layer_name}") except Exception as e: logger.warning(f"Failed to get OCG layers: {e}") return hidden_layers def _calculate_garble_rate(self, text: str) -> float: """ Calculate the rate of garbled characters in text. Detects: - (cid:xxxx) patterns (missing ToUnicode map) - Replacement character U+FFFD - Private Use Area (PUA) characters Args: text: Text to analyze Returns: Garble rate as float between 0.0 and 1.0 """ if not text: return 0.0 # Count (cid:xxxx) patterns cid_pattern = r'\(cid:\d+\)' cid_matches = re.findall(cid_pattern, text) cid_char_count = sum(len(m) for m in cid_matches) # Count replacement characters (U+FFFD) replacement_count = text.count('\ufffd') # Count Private Use Area characters (U+E000 to U+F8FF) pua_count = sum(1 for c in text if 0xE000 <= ord(c) <= 0xF8FF) total_garble = cid_char_count + replacement_count + pua_count total_chars = len(text) return total_garble / total_chars if total_chars > 0 else 0.0 def _should_fallback_to_ocr(self, page_text: str, page_num: int) -> bool: """ Determine if page should use OCR fallback based on garble rate. Args: page_text: Extracted text from page page_num: Page number for logging Returns: True if OCR fallback is recommended """ if not self.enable_garble_detection: return False garble_rate = self._calculate_garble_rate(page_text) if garble_rate > self.garble_ocr_fallback_threshold: logger.warning( f"Page {page_num}: High garble rate detected ({garble_rate:.1%}). " f"OCR fallback recommended." ) return True return False def _is_page_number(self, text: str) -> bool: """ Check if text is likely a page number. Args: text: Text to check Returns: True if text matches page number patterns """ text = text.strip() # Pure number if text.isdigit() and len(text) <= 4: return True # Common patterns patterns = [ r'^page\s*\d+$', # "Page 1" r'^-?\s*\d+\s*-?$', # "- 1 -" or "-1-" r'^\d+\s*/\s*\d+$', # "1/10" r'^第\s*\d+\s*[頁页]$', # "第1頁" or "第1页" r'^p\.?\s*\d+$', # "P.1" or "p1" ] for pattern in patterns: if re.match(pattern, text, re.IGNORECASE): return True return False def _filter_page_numbers(self, elements: List[DocumentElement], page_height: float) -> List[DocumentElement]: """ Filter out page number elements. Page numbers are typically: - In the bottom 10% of the page - Match numeric/page number patterns Args: elements: List of document elements page_height: Page height for position calculation Returns: Filtered list without page numbers """ if not self.enable_page_number_filter: return elements filtered = [] removed_count = 0 for elem in elements: # Only filter text elements if elem.type not in [ElementType.TEXT, ElementType.PARAGRAPH]: filtered.append(elem) continue # Check position - must be in bottom 10% of page if elem.bbox: y_rel = elem.bbox.y0 / page_height if y_rel > 0.90: # Get text content text = elem.get_text() if hasattr(elem, 'get_text') else str(elem.content) if self._is_page_number(text): removed_count += 1 logger.debug(f"Filtered page number: '{text}'") continue filtered.append(elem) if removed_count > 0: logger.info(f"Filtered {removed_count} page number element(s)") return filtered def _calculate_iou(self, bbox1: List[float], bbox2: List[float]) -> float: """ Calculate Intersection over Union (IoU) for two bounding boxes. Args: bbox1: First bounding box [x0, y0, x1, y1] bbox2: Second bounding box [x0, y0, x1, y1] Returns: IoU value between 0.0 and 1.0 """ # Calculate intersection x0 = max(bbox1[0], bbox2[0]) y0 = max(bbox1[1], bbox2[1]) x1 = min(bbox1[2], bbox2[2]) y1 = min(bbox1[3], bbox2[3]) # No intersection if x0 >= x1 or y0 >= y1: return 0.0 intersection = (x1 - x0) * (y1 - y0) # Calculate areas area1 = (bbox1[2] - bbox1[0]) * (bbox1[3] - bbox1[1]) area2 = (bbox2[2] - bbox2[0]) * (bbox2[3] - bbox2[1]) # Calculate union union = area1 + area2 - intersection if union <= 0: return 0.0 return intersection / union def _is_text_in_covered_regions(self, bbox: BoundingBox, covered_bboxes: List[fitz.Rect]) -> bool: """ Check if a text bbox overlaps with any covered (white-out) regions. Args: bbox: Text bounding box covered_bboxes: List of covered region rectangles Returns: True if text overlaps with covered regions """ if not covered_bboxes or not bbox: return False text_rect = fitz.Rect(bbox.x0, bbox.y0, bbox.x1, bbox.y1) for covered_rect in covered_bboxes: if text_rect.intersects(covered_rect): # Calculate overlap ratio intersection = text_rect & covered_rect if not intersection.is_empty: text_area = text_rect.width * text_rect.height if text_area > 0: overlap_ratio = (intersection.width * intersection.height) / text_area if overlap_ratio >= self.whiteout_iou_threshold: return True return False # ========================================================================= # Phase 4: GS Distillation - Exception Handler # ========================================================================= @staticmethod def is_ghostscript_available() -> bool: """Check if Ghostscript is available on the system.""" import shutil return shutil.which('gs') is not None def _should_trigger_gs_repair(self, file_path: Path) -> Tuple[bool, str]: """ Determine if Ghostscript repair should be triggered. Triggers on: 1. High garble rate (>10% cid:xxxx patterns) in extracted text 2. Severe mupdf structural errors during opening Args: file_path: Path to PDF file Returns: Tuple of (should_repair, reason) """ import io import sys reason = "" try: # Capture mupdf warnings old_stderr = sys.stderr sys.stderr = captured_stderr = io.StringIO() doc = fitz.open(str(file_path)) # Restore stderr and get warnings sys.stderr = old_stderr warnings = captured_stderr.getvalue() # Check for severe structural errors severe_keywords = ['error', 'invalid xref', 'corrupt', 'damaged', 'repair'] for keyword in severe_keywords: if keyword.lower() in warnings.lower(): reason = f"Structural error detected: {keyword}" doc.close() return True, reason # Check garble rate on first page if len(doc) > 0: page = doc[0] text = page.get_text("text") garble_rate = self._calculate_garble_rate(text) if garble_rate > self.garble_ocr_fallback_threshold: reason = f"High garble rate: {garble_rate:.1%}" doc.close() return True, reason doc.close() return False, "" except Exception as e: reason = f"Error opening PDF: {str(e)}" return True, reason def _repair_pdf_with_gs(self, input_path: Path, output_path: Path) -> bool: """ Repair a PDF using Ghostscript distillation. This re-renders the PDF through Ghostscript's PDF interpreter, which can fix many structural issues. Args: input_path: Path to input PDF output_path: Path to save repaired PDF Returns: True if repair succeeded, False otherwise """ import subprocess import shutil if not self.is_ghostscript_available(): logger.warning("Ghostscript not available, cannot repair PDF") return False try: # GS command for PDF repair/distillation cmd = [ 'gs', '-dNOPAUSE', '-dBATCH', '-dSAFER', '-sDEVICE=pdfwrite', '-dPDFSETTINGS=/prepress', '-dDetectDuplicateImages=true', '-dCompressFonts=true', '-dSubsetFonts=true', f'-sOutputFile={output_path}', str(input_path) ] logger.info(f"Running Ghostscript repair: {' '.join(cmd)}") result = subprocess.run( cmd, capture_output=True, text=True, timeout=60 # 60 second timeout ) if result.returncode == 0 and output_path.exists(): logger.info(f"Ghostscript repair successful: {output_path}") return True else: logger.error(f"Ghostscript repair failed: {result.stderr}") return False except subprocess.TimeoutExpired: logger.error("Ghostscript repair timed out") return False except Exception as e: logger.error(f"Ghostscript repair error: {e}") return False def extract_with_repair(self, file_path: Path, output_dir: Optional[Path] = None, enable_gs_repair: bool = False) -> UnifiedDocument: """ Extract content with optional Ghostscript repair for damaged PDFs. This method first checks if the PDF needs repair, and if so, attempts to repair it using Ghostscript before extraction. Args: file_path: Path to PDF file output_dir: Optional directory to save extracted images enable_gs_repair: Whether to attempt GS repair on problematic PDFs Returns: UnifiedDocument with extracted content """ import tempfile # Check if repair is needed and enabled if enable_gs_repair: should_repair, reason = self._should_trigger_gs_repair(file_path) if should_repair: logger.warning(f"PDF repair triggered: {reason}") if self.is_ghostscript_available(): # Create temporary file for repaired PDF with tempfile.NamedTemporaryFile(suffix='.pdf', delete=False) as tmp: tmp_path = Path(tmp.name) try: if self._repair_pdf_with_gs(file_path, tmp_path): logger.info("Using repaired PDF for extraction") result = self.extract(tmp_path, output_dir) # Add repair metadata if result.metadata: result.metadata.gs_repaired = True return result else: logger.warning("GS repair failed, trying original file") finally: # Cleanup temp file if tmp_path.exists(): tmp_path.unlink() else: logger.warning("Ghostscript not available, skipping repair") # Normal extraction return self.extract(file_path, output_dir) def get_pages_needing_ocr(self, doc: UnifiedDocument) -> List[int]: """ Get list of page numbers that need OCR fallback. This method checks each page's metadata for the 'needs_ocr_fallback' flag set during extraction when high garble rates are detected. Args: doc: UnifiedDocument from extraction Returns: List of page numbers (1-indexed) that need OCR processing """ pages_needing_ocr = [] for page in doc.pages: if page.metadata and page.metadata.get('needs_ocr_fallback', False): pages_needing_ocr.append(page.page_number) if pages_needing_ocr: logger.info(f"Pages needing OCR fallback: {pages_needing_ocr}") return pages_needing_ocr def get_extraction_quality_report(self, doc: UnifiedDocument) -> Dict[str, Any]: """ Generate a quality report for the extraction. This report helps determine if additional processing (OCR, manual review) is needed. Args: doc: UnifiedDocument from extraction Returns: Dict with quality metrics: - total_pages: int - pages_with_issues: list of page numbers with problems - average_garble_rate: float - needs_ocr_fallback: bool (any page needs OCR) - preprocessing_stats: dict with sanitization/whiteout counts """ report = { 'total_pages': len(doc.pages), 'pages_with_issues': [], 'garble_rates': {}, 'average_garble_rate': 0.0, 'needs_ocr_fallback': False, 'preprocessing_stats': { 'pages_sanitized': 0, 'total_whiteout_regions': 0, 'total_covering_images': 0 } } total_garble = 0.0 pages_with_garble = 0 for page in doc.pages: metadata = page.metadata or {} # Check garble rate garble_rate = metadata.get('garble_rate', 0.0) if garble_rate > 0: report['garble_rates'][page.page_number] = garble_rate total_garble += garble_rate pages_with_garble += 1 # Check OCR fallback flag if metadata.get('needs_ocr_fallback', False): report['pages_with_issues'].append(page.page_number) report['needs_ocr_fallback'] = True # Preprocessing stats preprocessing = metadata.get('preprocessing', {}) if preprocessing.get('sanitized', False): report['preprocessing_stats']['pages_sanitized'] += 1 report['preprocessing_stats']['total_whiteout_regions'] += preprocessing.get('whiteout_regions_found', 0) report['preprocessing_stats']['total_covering_images'] += preprocessing.get('covering_images_found', 0) # Calculate average garble rate if pages_with_garble > 0: report['average_garble_rate'] = total_garble / pages_with_garble return report