fix: OCR track table data format and image cropping
Table data format fixes (ocr_to_unified_converter.py): - Fix ElementType string conversion using value-based lookup - Add content-based HTML table detection (reclassify TEXT to TABLE) - Use BeautifulSoup for robust HTML table parsing - Generate TableData with fully populated cells arrays Image cropping for OCR track (pp_structure_enhanced.py): - Add _crop_and_save_image method for extracting image regions - Pass source_image_path to _process_parsing_res_list - Return relative filename (not full path) for saved_path - Consistent with Direct Track image saving pattern Also includes: - Add beautifulsoup4 to requirements.txt - Add architecture overview documentation - Archive fix-ocr-track-table-data-format proposal (22/24 tasks) Known issues: OCR track images are restored but still have quality issues that will be addressed in a follow-up proposal. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
egg
@@ -350,7 +350,19 @@ class OCRToUnifiedConverter:
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element_type = elem_data.get('type', ElementType.TEXT)
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if isinstance(element_type, str):
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# Convert string to ElementType if needed
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element_type = ElementType[element_type] if element_type in ElementType.__members__ else ElementType.TEXT
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# ElementType is a str-based enum, so we can construct from value (lowercase)
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try:
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element_type = ElementType(element_type)
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except ValueError:
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# If value doesn't match, try member name (uppercase)
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element_type = ElementType[element_type.upper()] if element_type.upper() in ElementType.__members__ else ElementType.TEXT
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# Content-based reclassification: detect HTML tables in text content
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content_str = elem_data.get('content', '')
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if isinstance(content_str, str) and '<table' in content_str.lower():
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if element_type == ElementType.TEXT:
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logger.info(f"Element {elem_data.get('element_id')}: Reclassifying TEXT to TABLE (HTML table in content)")
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element_type = ElementType.TABLE
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# Prepare content based on element type
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if element_type == ElementType.TABLE:
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@@ -538,7 +550,12 @@ class OCRToUnifiedConverter:
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return None
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def _extract_table_data(self, elem_data: Dict) -> Optional[TableData]:
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"""Extract table data from element."""
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"""
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Extract table data from element using BeautifulSoup for robust HTML parsing.
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This method produces TableData objects with fully populated cells arrays,
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matching the format produced by DirectExtractionEngine for consistency.
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"""
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try:
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html = elem_data.get('html', '')
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extracted_text = elem_data.get('extracted_text', '')
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@@ -550,31 +567,101 @@ class OCRToUnifiedConverter:
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html = content
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logger.debug("Using content field as HTML table source")
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# Try to parse HTML to get rows and columns
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rows = 0
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cols = 0
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cells = []
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# Return None if no HTML table content
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if not html or '<table' not in html.lower():
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if extracted_text:
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# Return minimal TableData with just caption if we have text
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return TableData(rows=0, cols=0, cells=[], caption=extracted_text)
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return None
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if html:
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# Simple HTML parsing (could be enhanced with BeautifulSoup)
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# Parse HTML table using BeautifulSoup
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try:
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from bs4 import BeautifulSoup
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soup = BeautifulSoup(html, 'html.parser')
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table = soup.find('table')
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if not table:
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logger.warning("No <table> element found in HTML")
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return self._fallback_table_data(html, extracted_text)
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cells = []
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headers = []
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rows = table.find_all('tr')
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# Track actual column positions accounting for rowspan/colspan
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# This is a simplified approach - complex spanning may need enhancement
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for row_idx, row in enumerate(rows):
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row_cells = row.find_all(['td', 'th'])
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col_idx = 0
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for cell in row_cells:
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cell_content = cell.get_text(strip=True)
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rowspan = int(cell.get('rowspan', 1))
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colspan = int(cell.get('colspan', 1))
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cells.append(TableCell(
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row=row_idx,
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col=col_idx,
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row_span=rowspan,
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col_span=colspan,
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content=cell_content
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))
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# Collect headers from <th> elements or first row
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if cell.name == 'th' or row_idx == 0:
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headers.append(cell_content)
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# Advance column index by colspan
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col_idx += colspan
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# Calculate actual dimensions
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num_rows = len(rows)
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num_cols = max(
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sum(int(cell.get('colspan', 1)) for cell in row.find_all(['td', 'th']))
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for row in rows
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) if rows else 0
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logger.debug(
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f"Parsed HTML table: {num_rows} rows, {num_cols} cols, {len(cells)} cells"
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)
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return TableData(
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rows=num_rows,
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cols=num_cols,
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cells=cells,
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headers=headers if headers else None,
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caption=extracted_text if extracted_text else None
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)
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except ImportError:
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logger.warning("BeautifulSoup not available, using fallback parsing")
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return self._fallback_table_data(html, extracted_text)
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except Exception as e:
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logger.warning(f"Failed to extract table data: {e}")
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return None
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def _fallback_table_data(self, html: str, extracted_text: str = '') -> Optional[TableData]:
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"""
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Fallback table parsing when BeautifulSoup is not available.
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Returns basic TableData with row/col counts only (no cells).
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"""
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try:
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rows = html.count('<tr')
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cols = 0
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if rows > 0:
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# Estimate columns from first row
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first_row_end = html.find('</tr>')
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if first_row_end > 0:
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first_row = html[:first_row_end]
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cols = first_row.count('<td') + first_row.count('<th')
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# Return None if no valid table data found
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if rows == 0 and cols == 0 and not extracted_text:
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return None
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# Note: TableData uses 'cols' not 'columns'
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# HTML content can be stored as caption or in element metadata
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return TableData(
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rows=rows,
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cols=cols,
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cells=cells,
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cells=[], # Empty cells in fallback mode
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caption=extracted_text if extracted_text else None
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)
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except:
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@@ -653,9 +740,9 @@ class OCRToUnifiedConverter:
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min_distance = float('inf')
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for target in targets:
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# Caption should be below the target
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if target.bbox.y2 <= caption.bbox.y1:
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distance = caption.bbox.y1 - target.bbox.y2
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# Caption should be below the target (y1 is bottom in BoundingBox)
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if target.bbox.y1 <= caption.bbox.y0:
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distance = caption.bbox.y0 - target.bbox.y1
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if distance < min_distance:
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min_distance = distance
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best_target = target
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@@ -684,8 +771,8 @@ class OCRToUnifiedConverter:
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else:
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prev_item = list_items[i-1]
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# Check if items are consecutive (similar x position, reasonable y gap)
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x_aligned = abs(item.bbox.x1 - prev_item.bbox.x1) < 20
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y_consecutive = (item.bbox.y1 - prev_item.bbox.y2) < 30
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x_aligned = abs(item.bbox.x0 - prev_item.bbox.x0) < 20
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y_consecutive = (item.bbox.y0 - prev_item.bbox.y1) < 30
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if x_aligned and y_consecutive:
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current_group.append(item)
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@@ -714,11 +801,11 @@ class OCRToUnifiedConverter:
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if i + 1 < len(headers):
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next_header_y = headers[i + 1].bbox.y1
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# Find all elements between headers
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# Find all elements between headers (y0=top, y1=bottom)
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content_elements = [
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e for e in elements
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if (e.bbox.y1 > header.bbox.y2 and
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e.bbox.y1 < next_header_y and
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if (e.bbox.y0 > header.bbox.y1 and
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e.bbox.y0 < next_header_y and
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e.type not in [ElementType.HEADER, ElementType.TITLE])
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]
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@@ -167,7 +167,7 @@ class PPStructureEnhanced:
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# Process parsing_res_list if found
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if parsing_res_list:
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elements = self._process_parsing_res_list(
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parsing_res_list, current_page, output_dir
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parsing_res_list, current_page, output_dir, image_path
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)
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all_elements.extend(elements)
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@@ -229,7 +229,8 @@ class PPStructureEnhanced:
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self,
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parsing_res_list: List[Dict],
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current_page: int,
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output_dir: Optional[Path]
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output_dir: Optional[Path],
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source_image_path: Optional[Path] = None
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) -> List[Dict[str, Any]]:
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"""
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Process parsing_res_list to extract all elements.
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@@ -238,6 +239,7 @@ class PPStructureEnhanced:
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parsing_res_list: List of parsed elements from PP-StructureV3
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current_page: Current page number
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output_dir: Optional output directory
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source_image_path: Path to source image for cropping image regions
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Returns:
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List of processed elements with normalized structure
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@@ -327,6 +329,17 @@ class PPStructureEnhanced:
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element['img_path'] = item['img_path'] # Keep original for reference
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else:
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logger.warning(f"Failed to save image for element {element['element_id']}")
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# Crop image from source if no img_path but source image is available
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elif source_image_path and output_dir and bbox != [0, 0, 0, 0]:
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cropped_path = self._crop_and_save_image(
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source_image_path, bbox, output_dir, element['element_id']
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)
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if cropped_path:
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element['saved_path'] = cropped_path
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element['img_path'] = cropped_path
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logger.info(f"Cropped and saved image region for {element['element_id']}")
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else:
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logger.warning(f"Failed to crop image for element {element['element_id']}")
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# Add any additional metadata
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if 'metadata' in item:
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@@ -536,3 +549,61 @@ class PPStructureEnhanced:
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logger.info(f"Saved image to {img_path}")
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except Exception as e:
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logger.warning(f"Failed to save PIL image: {e}")
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def _crop_and_save_image(
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self,
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source_image_path: Path,
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bbox: List[float],
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output_dir: Path,
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element_id: str
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) -> Optional[str]:
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"""
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Crop image region from source image and save to output directory.
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Args:
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source_image_path: Path to the source image
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bbox: Bounding box [x1, y1, x2, y2]
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output_dir: Output directory for saving cropped image
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element_id: Element ID for naming
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Returns:
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Relative filename (not full path) to saved image, consistent with
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Direct Track which stores "filename.png" that gets joined with
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result_dir by pdf_generator_service.
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"""
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try:
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from PIL import Image
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# Open source image
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with Image.open(source_image_path) as img:
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# Ensure bbox values are integers
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x1, y1, x2, y2 = [int(v) for v in bbox[:4]]
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# Validate bbox
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img_width, img_height = img.size
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x1 = max(0, min(x1, img_width))
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x2 = max(0, min(x2, img_width))
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y1 = max(0, min(y1, img_height))
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y2 = max(0, min(y2, img_height))
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if x2 <= x1 or y2 <= y1:
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logger.warning(f"Invalid bbox for cropping: {bbox}")
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return None
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# Crop the region
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cropped = img.crop((x1, y1, x2, y2))
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# Save directly to output directory (no subdirectory)
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# Consistent with Direct Track which saves to output_dir directly
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image_filename = f"{element_id}.png"
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img_path = output_dir / image_filename
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cropped.save(str(img_path), "PNG")
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# Return just the filename (relative to result_dir)
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# PDF generator will join with result_dir to get full path
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logger.info(f"Cropped image saved: {img_path} ({x2-x1}x{y2-y1} pixels)")
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return image_filename
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except Exception as e:
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logger.error(f"Failed to crop and save image for {element_id}: {e}")
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return None
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84
docs/architecture-overview.md
Normal file
84
docs/architecture-overview.md
Normal file
@@ -0,0 +1,84 @@
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# Tool_OCR 架構說明與 UML
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本文件概覽 Tool_OCR 的主要組件、資料流與雙軌處理（OCR / Direct），並附上 UML 關係圖以協助判斷改動的影響範圍。
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## 系統分層與重點元件
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- **API 層（FastAPI）**：`app/main.py` 啟動應用、掛載路由（`routers/auth.py`, `routers/tasks.py`, `routers/admin.py`），並在 lifespan 初始化記憶體管理、服務池與併發控制。
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- **任務/檔案管理**：`task_service.py` 與 `file_access_service.py` 掌管任務 CRUD、路徑與權限；`Task` / `TaskFile` 模型紀錄結果檔路徑。
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- **核心處理服務**：`OCRService`（`services/ocr_service.py`）負責雙軌路由與 OCR；整合偵測、直抽、OCR、統一格式轉換、匯出與 PDF 生成。
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- **雙軌偵測/直抽**：`DocumentTypeDetector` 判斷走 Direct 或 OCR；`DirectExtractionEngine` 使用 PyMuPDF 直接抽取文字/表格/圖片（必要時觸發混合模式補抽圖片）。
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- **OCR 解析**：PaddleOCR + `PPStructureEnhanced` 抽取 23 類元素；`OCRToUnifiedConverter` 轉成 `UnifiedDocument` 統一格式。
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- **匯出/呈現**：`UnifiedDocumentExporter` 產出 JSON/Markdown；`pdf_generator_service.py` 產生版面保持 PDF；前端透過 `/api/v2/tasks/{id}/download/*` 取得。
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- **資源控管**：`memory_manager.py`（MemoryGuard、prediction semaphore、模型生命週期），`service_pool.py`（`OCRService` 池）避免多重載模與 GPU 爆滿。
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## 處理流程（任務層級）
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1. **上傳**：`POST /api/v2/upload` 建立 Task 並寫檔到 `uploads/`（含 SHA256、檔案資訊）。
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2. **啟動**：`POST /api/v2/tasks/{id}/start`（`ProcessingOptions`，可含 `pp_structure_params`）→ 背景 `process_task_ocr` 取得服務池中的 `OCRService`。
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3. **軌道決策**：`DocumentTypeDetector.detect` 分析 MIME、PDF 文字覆蓋率或 Office 轉 PDF 後的抽樣結果：
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- **Direct**：`DirectExtractionEngine.extract` 產出 `UnifiedDocument`；若偵測缺圖則啟用混合模式呼叫 OCR 抽圖或渲染 inline 圖。
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- **OCR**：`process_file_traditional` → PaddleOCR + PP-Structure → `OCRToUnifiedConverter.convert` 產生 `UnifiedDocument`。
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- 以 `ProcessingTrack` 記錄 `ocr` / `direct` / `hybrid`，處理時間與統計寫入 metadata。
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4. **輸出保存**：`UnifiedDocumentExporter` 寫 `_result.json`（含 metadata、statistics）與 `_output.md`；`pdf_generator_service` 產出 `_layout.pdf`；路徑回寫 DB。
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5. **下載/檢視**：前端透過 `/download/json|markdown|pdf|unified` 取檔；`/metadata` 讀 JSON metadata 回傳統計與 `processing_track`。
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## 前端流程摘要
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- `UploadPage`：呼叫 `apiClientV2.uploadFile`，首個 `task_id` 存於 `uploadStore.batchId`。
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- `ProcessingPage`：對 `batchId` 呼叫 `startTask`（預設 `use_dual_track=true`，支援自訂 `pp_structure_params`），輪詢狀態。
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- `ResultsPage` / `TaskDetailPage`：使用 `getTask` 與 `getProcessingMetadata` 顯示 `processing_track`、統計並提供 JSON/Markdown/PDF/Unified 下載。
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- `TaskHistoryPage`：列出任務、支援重新啟動、重試、下載。
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## 共同模組與影響點
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- **UnifiedDocument**（`models/unified_document.py`）為 Direct/OCR 共用輸出格式；所有匯出/PDF/前端 track 顯示依賴其欄位與 metadata。
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- **服務池/記憶體守護**：Direct 與 OCR 共用同一 `OCRService` 實例池與 MemoryGuard；新增資源或改動需確保遵循 acquire/release、清理與 semaphore 規則。
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- **偵測閾值變更**：`DocumentTypeDetector` 參數調整會影響 Direct 與 OCR 分流比例，間接改變 GPU 載荷與結果格式。
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- **匯出/PDF**：任何 UnifiedDocument 結構變動會影響 JSON/Markdown/PDF 產出與前端下載/預覽；需同步維護轉換與匯出器。
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## UML 關係圖（Mermaid）
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```mermaid
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classDiagram
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class TasksRouter {
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+upload_file()
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+start_task()
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+download_json/markdown/pdf/unified()
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+get_metadata()
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}
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class TaskService {+create_task(); +update_task_status(); +get_task_by_id()}
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class FileAccessService
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class OCRService {
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+process()
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+process_with_dual_track()
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+process_file_traditional()
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+save_results()
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}
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class DocumentTypeDetector {+detect()}
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class DirectExtractionEngine {+extract(); +check_document_for_missing_images()}
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class OCRToUnifiedConverter {+convert()}
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class UnifiedDocument
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class UnifiedDocumentExporter {+export_to_json(); +export_to_markdown()}
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class PDFGeneratorService {+generate_layout_pdf(); +generate_from_unified_document()}
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class ServicePool {+acquire(); +release()}
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class MemoryManager <<singleton>>
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class OfficeConverter {+convert_to_pdf()}
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class PPStructureEnhanced {+analyze_with_full_structure()}
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TasksRouter --> TaskService
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TasksRouter --> FileAccessService
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TasksRouter --> OCRService : background process via process_task_ocr
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OCRService --> DocumentTypeDetector : track recommendation
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OCRService --> DirectExtractionEngine : direct track
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OCRService --> OCRToUnifiedConverter : OCR track result -> UnifiedDocument
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OCRService --> OfficeConverter : Office -> PDF
|
||||
OCRService --> PPStructureEnhanced : layout analysis (PP-StructureV3)
|
||||
OCRService --> UnifiedDocumentExporter : persist results
|
||||
OCRService --> PDFGeneratorService : layout-preserving PDF
|
||||
OCRService --> ServicePool : acquired instance
|
||||
ServicePool --> MemoryManager : model lifecycle / GPU guard
|
||||
UnifiedDocumentExporter --> UnifiedDocument
|
||||
PDFGeneratorService --> UnifiedDocument
|
||||
```
|
||||
|
||||
## 影響判斷指引
|
||||
- **改 Direct/偵測邏輯**：會改變 `processing_track` 與結果格式；前端顯示與下載 JSON/Markdown/PDF 仍依賴 UnifiedDocument，需驗證匯出與 PDF 生成。
|
||||
- **改 OCR/PP-Structure 參數**：僅影響 OCR track；Direct track 不受 `pp_structure_params` 影響（符合 spec），需維持 `processing_track` 填寫。
|
||||
- **改 UnifiedDocument 結構/統計**：需同步 `UnifiedDocumentExporter`、`pdf_generator_service`、前端 `getProcessingMetadata`/下載端點。
|
||||
- **改資源控管**：服務池或 MemoryGuard 調整會同時影響 Direct/OCR 執行時序與穩定性，須確保 acquire/release 與 semaphore 不被破壞。
|
||||
@@ -0,0 +1,173 @@
|
||||
# Design: Fix OCR Track Table Data Format
|
||||
|
||||
## Context
|
||||
|
||||
The OCR processing pipeline has three modes:
|
||||
1. **Direct Track**: Extracts structured data directly from native PDFs using `direct_extraction_engine.py`
|
||||
2. **OCR Track**: Uses PP-StructureV3 for layout analysis and OCR, then converts results via `ocr_to_unified_converter.py`
|
||||
3. **Hybrid Mode**: Uses Direct Track as primary, supplements with OCR Track for missing images only
|
||||
|
||||
Both tracks produce `UnifiedDocument` containing `DocumentElement` objects. For tables, the `content` field should contain a `TableData` object with populated `cells` array. However, OCR Track currently produces `TableData` with empty `cells`, causing PDF generation failures.
|
||||
|
||||
## Track Isolation Analysis (Safety Guarantee)
|
||||
|
||||
This section documents why the proposed changes will NOT affect Direct Track or Hybrid Mode.
|
||||
|
||||
### Code Flow Analysis
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────────┐
|
||||
│ ocr_service.py │
|
||||
├─────────────────────────────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ Direct Track ──► DirectExtractionEngine ──► UnifiedDocument │
|
||||
│ (direct_extraction_engine.py) (tables: TableData ✓) │
|
||||
│ [NOT MODIFIED] │
|
||||
│ │
|
||||
│ OCR Track ────► PP-StructureV3 ──► OCRToUnifiedConverter ──► UnifiedDoc│
|
||||
│ (ocr_to_unified_converter.py) │
|
||||
│ [MODIFIED: _extract_table_data] │
|
||||
│ │
|
||||
│ Hybrid Mode ──► Direct Track (primary) + OCR Track (images only) │
|
||||
│ │ │ │
|
||||
│ │ └──► _merge_ocr_images_into_ │
|
||||
│ │ direct() merges ONLY: │
|
||||
│ │ - ElementType.FIGURE │
|
||||
│ │ - ElementType.IMAGE │
|
||||
│ │ - ElementType.LOGO │
|
||||
│ │ [Tables NOT merged] │
|
||||
│ └──► Tables come from Direct Track (unchanged) │
|
||||
└─────────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
### Evidence from ocr_service.py
|
||||
|
||||
**Line 1610** (Hybrid mode merge logic):
|
||||
```python
|
||||
image_types = {ElementType.FIGURE, ElementType.IMAGE, ElementType.LOGO}
|
||||
```
|
||||
|
||||
**Lines 1634-1635** (Only image types are merged):
|
||||
```python
|
||||
for element in ocr_page.elements:
|
||||
if element.type in image_types: # Tables excluded
|
||||
```
|
||||
|
||||
### Impact Matrix
|
||||
|
||||
| Mode | Table Source | Uses OCRToUnifiedConverter? | Affected by Change? |
|
||||
|------|--------------|----------------------------|---------------------|
|
||||
| Direct Track | `DirectExtractionEngine` | No | **No** |
|
||||
| OCR Track | `OCRToUnifiedConverter` | Yes | **Yes (Fixed)** |
|
||||
| Hybrid Mode | `DirectExtractionEngine` (tables) | Only for images | **No** |
|
||||
|
||||
### Conclusion
|
||||
|
||||
The fix is **isolated to OCR Track only**:
|
||||
- Direct Track: Uses separate engine (`DirectExtractionEngine`), completely unaffected
|
||||
- Hybrid Mode: Tables come from Direct Track; OCR Track is only used for image extraction
|
||||
- OCR Track: Will benefit from the fix with proper `TableData` output
|
||||
|
||||
## Goals / Non-Goals
|
||||
|
||||
### Goals
|
||||
- OCR Track table output format matches Direct Track format exactly
|
||||
- PDF Generator receives consistent `TableData` objects from both tracks
|
||||
- Robust HTML table parsing that handles real-world OCR output
|
||||
|
||||
### Non-Goals
|
||||
- Modifying Direct Track behavior (it's the reference implementation)
|
||||
- Changing the `TableData` or `TableCell` data models
|
||||
- Modifying PDF Generator to handle HTML strings as a workaround
|
||||
|
||||
## Decisions
|
||||
|
||||
### Decision 1: Use BeautifulSoup for HTML Parsing
|
||||
|
||||
**Rationale**: The current regex/string-counting approach is fragile and cannot extract cell content. BeautifulSoup provides:
|
||||
- Robust handling of malformed HTML (common in OCR output)
|
||||
- Easy extraction of cell content, attributes (rowspan, colspan)
|
||||
- Well-tested library already used in many Python projects
|
||||
|
||||
**Alternatives considered**:
|
||||
- Manual regex parsing: Too fragile for complex tables
|
||||
- lxml: More complex API, overkill for this use case
|
||||
- html.parser (stdlib): Less tolerant of malformed HTML
|
||||
|
||||
### Decision 2: Maintain Backward Compatibility
|
||||
|
||||
**Rationale**: If BeautifulSoup parsing fails, fall back to current behavior (return `TableData` with basic row/col counts). This ensures existing functionality isn't broken.
|
||||
|
||||
### Decision 3: Single Point of Change
|
||||
|
||||
**Rationale**: Only modify `ocr_to_unified_converter.py`. This:
|
||||
- Minimizes regression risk
|
||||
- Keeps Direct Track untouched as reference
|
||||
- Requires no changes to downstream PDF Generator
|
||||
|
||||
## Implementation Approach
|
||||
|
||||
```python
|
||||
def _extract_table_data(self, elem_data: Dict) -> Optional[TableData]:
|
||||
"""Extract table data from element using BeautifulSoup."""
|
||||
try:
|
||||
html = elem_data.get('html', '') or elem_data.get('content', '')
|
||||
if not html or '<table' not in html.lower():
|
||||
return None
|
||||
|
||||
soup = BeautifulSoup(html, 'html.parser')
|
||||
table = soup.find('table')
|
||||
if not table:
|
||||
return None
|
||||
|
||||
cells = []
|
||||
headers = []
|
||||
rows = table.find_all('tr')
|
||||
|
||||
for row_idx, row in enumerate(rows):
|
||||
row_cells = row.find_all(['td', 'th'])
|
||||
for col_idx, cell in enumerate(row_cells):
|
||||
cell_content = cell.get_text(strip=True)
|
||||
rowspan = int(cell.get('rowspan', 1))
|
||||
colspan = int(cell.get('colspan', 1))
|
||||
|
||||
cells.append(TableCell(
|
||||
row=row_idx,
|
||||
col=col_idx,
|
||||
row_span=rowspan,
|
||||
col_span=colspan,
|
||||
content=cell_content
|
||||
))
|
||||
|
||||
# Collect headers from first row or <th> elements
|
||||
if row_idx == 0 or cell.name == 'th':
|
||||
headers.append(cell_content)
|
||||
|
||||
return TableData(
|
||||
rows=len(rows),
|
||||
cols=max(len(row.find_all(['td', 'th'])) for row in rows) if rows else 0,
|
||||
cells=cells,
|
||||
headers=headers if headers else None
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to parse HTML table: {e}")
|
||||
return None # Fallback handled by caller
|
||||
```
|
||||
|
||||
## Risks / Trade-offs
|
||||
|
||||
| Risk | Mitigation |
|
||||
|------|------------|
|
||||
| BeautifulSoup not installed | Add to requirements.txt; it's already a common dependency |
|
||||
| Malformed HTML causes parsing errors | Use try/except with fallback to current behavior |
|
||||
| Performance impact from HTML parsing | Minimal; tables are small; BeautifulSoup is fast |
|
||||
| Complex rowspan/colspan calculations | Start with simple col tracking; enhance if needed |
|
||||
|
||||
## Dependencies
|
||||
|
||||
- `beautifulsoup4`: Already commonly available, add to requirements.txt if not present
|
||||
|
||||
## Open Questions
|
||||
|
||||
- Q: Should we preserve the original HTML in metadata for debugging?
|
||||
- A: Optional enhancement; not required for initial fix
|
||||
@@ -0,0 +1,45 @@
|
||||
# Change: Fix OCR Track Table Data Format to Match Direct Track
|
||||
|
||||
## Why
|
||||
|
||||
OCR Track produces HTML strings for table content instead of structured `TableData` objects, causing PDF generation to render raw HTML code as plain text. Direct Track correctly produces `TableData` objects with populated `cells` array, resulting in proper table rendering. This inconsistency creates poor user experience when using OCR Track for documents containing tables.
|
||||
|
||||
## What Changes
|
||||
|
||||
- **Enhance `_extract_table_data` method** in `ocr_to_unified_converter.py` to properly parse HTML tables into structured `TableData` objects with populated `TableCell` arrays
|
||||
- **Add BeautifulSoup-based HTML table parsing** to robustly extract cell content, row/column spans from OCR-generated HTML tables
|
||||
- **Ensure format consistency** between OCR Track and Direct Track table output, allowing PDF Generator to handle a single standardized format
|
||||
|
||||
## Impact
|
||||
|
||||
- Affected specs: `ocr-processing`
|
||||
- Affected code:
|
||||
- `backend/app/services/ocr_to_unified_converter.py` (primary changes)
|
||||
- `backend/app/services/pdf_generator_service.py` (no changes needed - already handles `TableData`)
|
||||
- `backend/app/services/direct_extraction_engine.py` (no changes - serves as reference implementation)
|
||||
|
||||
## Evidence
|
||||
|
||||
### Direct Track (Reference - Correct Behavior)
|
||||
`direct_extraction_engine.py:846-850`:
|
||||
```python
|
||||
table_data = TableData(
|
||||
rows=len(data),
|
||||
cols=max(len(row) for row in data) if data else 0,
|
||||
cells=cells, # Properly populated with TableCell objects
|
||||
headers=data[0] if data else None
|
||||
)
|
||||
```
|
||||
|
||||
### OCR Track (Current - Problematic)
|
||||
`ocr_to_unified_converter.py:574-579`:
|
||||
```python
|
||||
return TableData(
|
||||
rows=rows, # Only counts from html.count('<tr')
|
||||
cols=cols, # Only counts from <td>/<th> in first row
|
||||
cells=cells, # Always empty list []
|
||||
caption=extracted_text
|
||||
)
|
||||
```
|
||||
|
||||
The `cells` array is always empty because the current HTML parsing only counts tags but doesn't extract actual cell content.
|
||||
@@ -0,0 +1,51 @@
|
||||
## ADDED Requirements
|
||||
|
||||
### Requirement: OCR Track Table Data Structure Consistency
|
||||
The OCR Track SHALL produce `TableData` objects with fully populated `cells` arrays that match the format produced by Direct Track, ensuring consistent table rendering across both processing tracks.
|
||||
|
||||
#### Scenario: OCR Track produces structured TableData for HTML tables
|
||||
- **GIVEN** a document with tables is processed via OCR Track
|
||||
- **WHEN** PP-StructureV3 returns HTML table content in the `html` or `content` field
|
||||
- **THEN** the `ocr_to_unified_converter` SHALL parse the HTML and produce a `TableData` object
|
||||
- **AND** the `TableData.cells` array SHALL contain `TableCell` objects for each cell
|
||||
- **AND** each `TableCell` SHALL have correct `row`, `col`, and `content` values
|
||||
- **AND** the output format SHALL match Direct Track's `TableData` structure
|
||||
|
||||
#### Scenario: OCR Track handles tables with merged cells
|
||||
- **GIVEN** an HTML table with `rowspan` or `colspan` attributes
|
||||
- **WHEN** the table is converted to `TableData`
|
||||
- **THEN** each `TableCell` SHALL have correct `row_span` and `col_span` values
|
||||
- **AND** the cell content SHALL be correctly extracted
|
||||
|
||||
#### Scenario: OCR Track handles header rows
|
||||
- **GIVEN** an HTML table with `<th>` elements or a header row
|
||||
- **WHEN** the table is converted to `TableData`
|
||||
- **THEN** the `TableData.headers` field SHALL contain the header cell contents
|
||||
- **AND** header cells SHALL also be included in the `cells` array
|
||||
|
||||
#### Scenario: OCR Track gracefully handles malformed HTML tables
|
||||
- **GIVEN** an HTML table with malformed markup (missing closing tags, invalid nesting)
|
||||
- **WHEN** parsing is attempted
|
||||
- **THEN** the system SHALL attempt best-effort parsing using a tolerant HTML parser
|
||||
- **AND** if parsing fails completely, SHALL fall back to returning basic `TableData` with row/col counts
|
||||
- **AND** SHALL log a warning for debugging purposes
|
||||
|
||||
#### Scenario: PDF Generator renders OCR Track tables correctly
|
||||
- **GIVEN** a `UnifiedDocument` from OCR Track containing table elements
|
||||
- **WHEN** the PDF Generator processes the document
|
||||
- **THEN** tables SHALL be rendered as formatted tables (not as raw HTML text)
|
||||
- **AND** the rendering SHALL be identical to Direct Track table rendering
|
||||
|
||||
#### Scenario: Direct Track table processing remains unchanged
|
||||
- **GIVEN** a native PDF with embedded tables
|
||||
- **WHEN** the document is processed via Direct Track
|
||||
- **THEN** the `DirectExtractionEngine` SHALL continue to produce `TableData` objects as before
|
||||
- **AND** the `ocr_to_unified_converter.py` changes SHALL NOT affect Direct Track processing
|
||||
- **AND** table rendering in PDF output SHALL be identical to pre-fix behavior
|
||||
|
||||
#### Scenario: Hybrid Mode table source isolation
|
||||
- **GIVEN** a document processed via Hybrid Mode (Direct Track primary + OCR Track for images)
|
||||
- **WHEN** the system merges OCR Track results into Direct Track results
|
||||
- **THEN** only image elements (FIGURE, IMAGE, LOGO) SHALL be merged from OCR Track
|
||||
- **AND** table elements SHALL exclusively come from Direct Track
|
||||
- **AND** no OCR Track table data SHALL contaminate the final output
|
||||
@@ -0,0 +1,43 @@
|
||||
# Tasks: Fix OCR Track Table Data Format
|
||||
|
||||
## 1. Implementation
|
||||
|
||||
- [x] 1.1 Add BeautifulSoup import and dependency check in `ocr_to_unified_converter.py`
|
||||
- [x] 1.2 Rewrite `_extract_table_data` method to parse HTML using BeautifulSoup
|
||||
- [x] 1.3 Extract cell content, row index, column index for each `<td>` and `<th>` element
|
||||
- [x] 1.4 Handle `rowspan` and `colspan` attributes for merged cells
|
||||
- [x] 1.5 Create `TableCell` objects with proper content and positioning
|
||||
- [x] 1.6 Populate `TableData.cells` array with extracted `TableCell` objects
|
||||
- [x] 1.7 Preserve header detection (`<th>` elements) and store in `TableData.headers`
|
||||
|
||||
## 2. Edge Case Handling
|
||||
|
||||
- [x] 2.1 Handle malformed HTML tables gracefully (missing closing tags, nested tables)
|
||||
- [x] 2.2 Handle empty cells (create TableCell with empty string content)
|
||||
- [x] 2.3 Handle tables without `<tr>` structure (fallback to current behavior)
|
||||
- [x] 2.4 Log warnings for unparseable tables instead of failing silently
|
||||
|
||||
## 3. Testing
|
||||
|
||||
- [x] 3.1 Create unit tests for `_extract_table_data` with various HTML table formats
|
||||
- [x] 3.2 Test simple tables (basic rows/columns)
|
||||
- [x] 3.3 Test tables with merged cells (rowspan/colspan)
|
||||
- [x] 3.4 Test tables with header rows (`<th>` elements)
|
||||
- [x] 3.5 Test malformed HTML tables (handled via BeautifulSoup's tolerance)
|
||||
- [ ] 3.6 Integration test: OCR Track PDF generation with tables
|
||||
|
||||
## 4. Verification (Track Isolation)
|
||||
|
||||
- [x] 4.1 Compare OCR Track table output format with Direct Track output format
|
||||
- [ ] 4.2 Verify PDF Generator renders OCR Track tables correctly
|
||||
- [x] 4.3 **Direct Track regression test**: `direct_extraction_engine.py` NOT modified (confirmed via git status)
|
||||
- [x] 4.4 **Hybrid Mode regression test**: `ocr_service.py` NOT modified, image merge logic unchanged
|
||||
- [x] 4.5 **OCR Track fix verification**: Unit tests confirm:
|
||||
- `TableData.cells` array is populated (6 cells in 3x2 table)
|
||||
- `TableCell` objects have correct row/col/content values
|
||||
- Headers extracted correctly
|
||||
- [x] 4.6 Verify `DirectExtractionEngine` code is NOT modified (isolation check - confirmed)
|
||||
|
||||
## 5. Dependencies
|
||||
|
||||
- [x] 5.1 Add `beautifulsoup4>=4.12.0` to `requirements.txt`
|
||||
@@ -69,3 +69,4 @@ pylint>=3.0.0
|
||||
|
||||
# ===== Utilities =====
|
||||
python-magic>=0.4.27 # File type detection
|
||||
beautifulsoup4>=4.12.0 # HTML table parsing for OCR track
|
||||
|
||||
Reference in New Issue
Block a user