feat: add TableCellMatcher for matching HTML table cells with PaddleOCR bounding boxes

- Implemented TableCellMatcher class to enhance HTML tables with bounding box information from PaddleOCR.
- Added methods for filtering, grouping, and matching text boxes to HTML table cells.
- Introduced strategies for estimating column boundaries and handling overlapping text boxes.
- Enhanced matching logic with sequential matching and fuzzy matching capabilities.
- Improved debugging output for better traceability during matching process.

merger/data_processor.py

@@ -8,15 +8,17 @@ from bs4 import BeautifulSoup
 try:
     from .text_matcher import TextMatcher
     from .bbox_extractor import BBoxExtractor
+    from .table_cell_matcher import TableCellMatcher
 except ImportError:
     from text_matcher import TextMatcher
     from bbox_extractor import BBoxExtractor
+    from table_cell_matcher import TableCellMatcher
 
 
 class DataProcessor:
     """数据处理器"""
     
-    def __init__(self, text_matcher: TextMatcher, look_ahead_window: int = 10, x_tolerance: int = 3):
+    def __init__(self, text_matcher: TextMatcher, look_ahead_window: int = 10, x_tolerance: int = 3, y_tolerance: int = 10):
         """
         Args:
             text_matcher: 文本匹配器
@@ -27,6 +29,14 @@ class DataProcessor:
         self.look_ahead_window = look_ahead_window
         # X轴容差, 用于判断文本框是否在同一列
         self.x_tolerance = x_tolerance
+        self.y_tolerance = y_tolerance  # Y轴容差, 用于行分组
+        
+        # 🎯 创建表格单元格匹配器
+        self.table_cell_matcher = TableCellMatcher(
+            text_matcher=text_matcher,
+            x_tolerance=x_tolerance,
+            y_tolerance=y_tolerance
+        )
     
     def process_mineru_data(self, mineru_data: List[Dict], 
                            paddle_text_boxes: List[Dict]) -> List[Dict]:
@@ -397,12 +407,14 @@ class DataProcessor:
             # 🔑 传入 table_bbox 用于筛选
             table_bbox = item.get('bbox')  # MinerU 提供的表格边界
             
-            enhanced_html, cells, new_pointer = self._enhance_table_html_with_bbox(
-                table_body,
-                paddle_text_boxes,
-                start_pointer,
-                table_bbox  # ✅ 传入边界框
-            )
+            # 🎯 委托给 TableCellMatcher
+            enhanced_html, cells, new_pointer = \
+                self.table_cell_matcher.enhance_table_html_with_bbox(
+                    table_body,
+                    paddle_text_boxes,
+                    start_pointer,
+                    table_bbox
+                )
             
             # 更新 item
             item['table_body'] = enhanced_html
@@ -456,831 +468,3 @@ class DataProcessor:
                 matched_bbox['used'] = True
         
         return merged_item, paddle_pointer, last_matched_index
-    
-    def _enhance_table_html_with_bbox(self, html: str, paddle_text_boxes: List[Dict],
-                                  start_pointer: int, table_bbox: Optional[List[int]] = None) -> Tuple[str, List[Dict], int]:
-        """
-        为 HTML 表格添加 bbox 信息（优化版：先筛选表格区域）
-        
-        策略：
-        1. 根据 table_bbox 筛选出表格区域内的 paddle_text_boxes
-        2. 将筛选后的 boxes 按行分组
-        3. 智能匹配 HTML 行与 paddle 行组
-        4. 在匹配的组内查找单元格
-    
-        Args:
-            html: HTML 表格
-            paddle_text_boxes: 全部 paddle OCR 结果
-            start_pointer: 开始位置
-            table_bbox: 表格边界框 [x1, y1, x2, y2]
-        """
-        soup = BeautifulSoup(html, 'html.parser')
-        cells = []
-        
-        # 🔑 第一步：筛选表格区域内的 paddle boxes
-        table_region_boxes, actual_table_bbox = self._filter_boxes_in_table_region(
-            paddle_text_boxes[start_pointer:],
-            table_bbox,
-            html
-        )
-        
-        if not table_region_boxes:
-            print(f"⚠️ 未在表格区域找到 paddle boxes")
-            return str(soup), cells, start_pointer
-        
-        print(f"📊 表格区域: {len(table_region_boxes)} 个文本框")
-        print(f"   边界: {actual_table_bbox}")
-        
-        # 🔑 第二步：将表格区域的 boxes 按行分组
-        grouped_boxes = self._group_paddle_boxes_by_rows(
-            table_region_boxes,
-            y_tolerance=20
-        )
-        
-        # 🔑 第三步：在每组内按 x 坐标排序
-        for group in grouped_boxes:
-            group['boxes'].sort(key=lambda x: x['bbox'][0])
-        
-        grouped_boxes.sort(key=lambda g: g['y_center'])
-        
-        print(f"   分组: {len(grouped_boxes)} 行")
-        
-        # 🔑 第四步：智能匹配 HTML 行与 paddle 行组
-        html_rows = soup.find_all('tr')
-        row_mapping = self._match_html_rows_to_paddle_groups(html_rows, grouped_boxes)
-        
-        print(f"   HTML行: {len(html_rows)} 行")
-        print(f"   映射: {len([v for v in row_mapping.values() if v])} 个有效映射")
-        
-        # 🔑 第五步：遍历 HTML 表格，使用映射关系查找
-        for row_idx, row in enumerate(html_rows):
-            group_indices = row_mapping.get(row_idx, [])
-            
-            if not group_indices:
-                continue
-            
-            # 合并多个组的 boxes
-            current_boxes = []
-            for group_idx in group_indices:
-                if group_idx < len(grouped_boxes):
-                    current_boxes.extend(grouped_boxes[group_idx]['boxes'])
-            
-            current_boxes.sort(key=lambda x: x['bbox'][0])
-            
-            # 🎯 关键改进：提取 HTML 单元格并预先确定列边界
-            html_cells = row.find_all(['td', 'th'])
-            
-            if not html_cells:
-                continue
-            
-            # 🔑 预估列边界（基于 x 坐标分布）
-            col_boundaries = self._estimate_column_boundaries(
-                current_boxes, 
-                len(html_cells)
-            )
-            
-            print(f"   行 {row_idx + 1}: {len(html_cells)} 列，边界: {col_boundaries}")
-            
-            # 🎯 关键改进：顺序指针匹配
-            box_pointer = 0  # 当前行的 boxes 指针
-            
-            for col_idx, cell in enumerate(html_cells):
-                cell_text = cell.get_text(strip=True)
-                
-                if not cell_text:
-                    continue
-                
-                # 🔑 从当前指针开始匹配
-                matched_result = self._match_cell_sequential(
-                    cell_text,
-                    current_boxes,
-                    col_boundaries,
-                    box_pointer
-                )
-                
-                if matched_result:
-                    merged_bbox = matched_result['bbox']
-                    merged_text = matched_result['text']
-                    
-                    cell['data-bbox'] = f"[{merged_bbox[0]},{merged_bbox[1]},{merged_bbox[2]},{merged_bbox[3]}]"
-                    cell['data-score'] = f"{matched_result['score']:.4f}"
-                    cell['data-paddle-indices'] = str(matched_result['paddle_indices'])
-                    
-                    cells.append({
-                        'type': 'table_cell',
-                        'text': cell_text,
-                        'matched_text': merged_text,
-                        'bbox': merged_bbox,
-                        'row': row_idx + 1,
-                        'col': col_idx + 1,
-                        'score': matched_result['score'],
-                        'paddle_bbox_indices': matched_result['paddle_indices']
-                    })
-                    
-                    # 标记已使用
-                    for box in matched_result['used_boxes']:
-                        box['used'] = True
-                    
-                    # 🎯 移动指针到最后使用的 box 之后
-                    box_pointer = matched_result['last_used_index'] + 1
-                    
-                    print(f"      列 {col_idx + 1}: '{cell_text[:20]}...' 匹配 {len(matched_result['used_boxes'])} 个box (指针: {box_pointer})")
-        
-        # 计算新的指针位置
-        used_count = sum(1 for box in table_region_boxes if box.get('used'))
-        new_pointer = start_pointer + used_count
-        
-        print(f"   匹配: {len(cells)} 个单元格")
-        
-        return str(soup), cells, new_pointer
-
-
-    def _estimate_column_boundaries(self, boxes: List[Dict], 
-                                    num_cols: int) -> List[Tuple[int, int]]:
-        """
-        估算列边界（改进版：处理同列多文本框）
-        
-        Args:
-            boxes: 当前行的所有 boxes（已按 x 排序）
-            num_cols: HTML 表格的列数
-        
-        Returns:
-            列边界列表 [(x_start, x_end), ...]
-        """
-        if not boxes:
-            return []
-        
-        # 🔑 关键改进：先按 x 坐标聚类（合并同列的多个文本框）
-        x_clusters = self._cluster_boxes_by_x(boxes, x_tolerance=self.x_tolerance)
-        
-        print(f"      X聚类: {len(boxes)} 个boxes -> {len(x_clusters)} 个列簇")
-        
-        # 获取所有 x 坐标范围
-        x_min = min(cluster['x_min'] for cluster in x_clusters)
-        x_max = max(cluster['x_max'] for cluster in x_clusters)
-        
-        # 🎯 策略 1: 如果聚类数量<=列数接近
-        if len(x_clusters) <= num_cols:
-            # 直接使用聚类边界
-            boundaries = [(cluster['x_min'], cluster['x_max']) 
-                        for cluster in x_clusters]
-            return boundaries
-        
-        # 🎯 策略 2: 聚类数多于列数（某些列有多个文本簇）
-        if len(x_clusters) > num_cols:
-            print(f"      ℹ️ 聚类数 {len(x_clusters)} > 列数 {num_cols}，合并相近簇")
-            
-            # 合并相近的簇
-            merged_clusters = self._merge_close_clusters(x_clusters, num_cols)
-            
-            boundaries = [(cluster['x_min'], cluster['x_max']) 
-                        for cluster in merged_clusters]
-            return boundaries
-        
-        return []
-
-
-    def _cluster_boxes_by_x(self, boxes: List[Dict], 
-                    x_tolerance: int = 3) -> List[Dict]:
-        """
-        按 x 坐标聚类（合并同列的多个文本框）
-        
-        Args:
-            boxes: 文本框列表
-            x_tolerance: X坐标容忍度
-        
-        Returns:
-            聚类列表 [{'x_min': int, 'x_max': int, 'boxes': List[Dict]}, ...]
-        """
-        if not boxes:
-            return []
-        
-        # 按左边界 x 坐标排序
-        sorted_boxes = sorted(boxes, key=lambda b: b['bbox'][0])
-        
-        clusters = []
-        current_cluster = None
-        
-        for box in sorted_boxes:
-            bbox = box['bbox']
-            x_start = bbox[0]
-            x_end = bbox[2]
-            
-            if current_cluster is None:
-                # 开始新簇
-                current_cluster = {
-                    'x_min': x_start,
-                    'x_max': x_end,
-                    'boxes': [box]
-                }
-            else:
-                # 🔑 检查是否属于当前簇（修正后的逻辑）
-                # 1. x 坐标有重叠：x_start <= current_x_max 且 x_end >= current_x_min
-                # 2. 或者距离在容忍度内
-            
-                has_overlap = (x_start <= current_cluster['x_max'] and 
-                              x_end >= current_cluster['x_min'])
-            
-                is_close = abs(x_start - current_cluster['x_max']) <= x_tolerance
-            
-                if has_overlap or is_close:
-                    # 合并到当前簇
-                    current_cluster['boxes'].append(box)
-                    current_cluster['x_min'] = min(current_cluster['x_min'], x_start)
-                    current_cluster['x_max'] = max(current_cluster['x_max'], x_end)
-                else:
-                    # 保存当前簇，开始新簇
-                    clusters.append(current_cluster)
-                    current_cluster = {
-                        'x_min': x_start,
-                        'x_max': x_end,
-                        'boxes': [box]
-                    }
-    
-        # 添加最后一簇
-        if current_cluster:
-            clusters.append(current_cluster)
-        
-        return clusters
-
-
-    def _merge_close_clusters(self, clusters: List[Dict], 
-                            target_count: int) -> List[Dict]:
-        """
-        合并相近的簇，直到数量等于目标列数
-        
-        Args:
-            clusters: 聚类列表
-            target_count: 目标列数
-        
-        Returns:
-            合并后的聚类列表
-        """
-        if len(clusters) <= target_count:
-            return clusters
-        
-        # 复制一份，避免修改原数据
-        working_clusters = [c.copy() for c in clusters]
-        
-        while len(working_clusters) > target_count:
-            # 找到距离最近的两个簇
-            min_distance = float('inf')
-            merge_idx = 0
-            
-            for i in range(len(working_clusters) - 1):
-                distance = working_clusters[i + 1]['x_min'] - working_clusters[i]['x_max']
-                if distance < min_distance:
-                    min_distance = distance
-                    merge_idx = i
-            
-            # 合并
-            cluster1 = working_clusters[merge_idx]
-            cluster2 = working_clusters[merge_idx + 1]
-            
-            merged_cluster = {
-                'x_min': cluster1['x_min'],
-                'x_max': cluster2['x_max'],
-                'boxes': cluster1['boxes'] + cluster2['boxes']
-            }
-            
-            # 替换
-            working_clusters[merge_idx] = merged_cluster
-            working_clusters.pop(merge_idx + 1)
-        
-        return working_clusters
-
-
-    def _get_boxes_in_column(self, boxes: List[Dict], 
-                            boundaries: List[Tuple[int, int]],
-                            col_idx: int) -> List[Dict]:
-        """
-        获取指定列范围内的 boxes（改进版：包含重叠）
-        
-        Args:
-            boxes: 当前行的所有 boxes
-            boundaries: 列边界
-            col_idx: 列索引
-        
-        Returns:
-            该列的 boxes
-        """
-        if col_idx >= len(boundaries):
-            return []
-        
-        x_start, x_end = boundaries[col_idx]
-        
-        col_boxes = []
-        for box in boxes:
-            bbox = box['bbox']
-            box_x_start = bbox[0]
-            box_x_end = bbox[2]
-            
-            # 🔑 改进：检查是否有重叠（不只是中心点）
-            overlap = not (box_x_start > x_end or box_x_end < x_start)
-            
-            if overlap:
-                col_boxes.append(box)
-        
-        return col_boxes
-
-
-    def _filter_boxes_in_table_region(self, paddle_boxes: List[Dict],
-                                  table_bbox: Optional[List[int]],
-                                  html: str) -> Tuple[List[Dict], List[int]]:
-        """
-        筛选表格区域内的 paddle boxes
-    
-        策略：
-        1. 如果有 table_bbox，使用边界框筛选（扩展边界）
-        2. 如果没有 table_bbox，通过内容匹配推断区域
-    
-        Args:
-            paddle_boxes: paddle OCR 结果
-            table_bbox: 表格边界框 [x1, y1, x2, y2]
-            html: HTML 内容（用于内容验证）
-    
-        Returns:
-            (筛选后的 boxes, 实际表格边界框)
-        """
-        if not paddle_boxes:
-            return [], [0, 0, 0, 0]
-        
-        # 🎯 策略 1: 使用提供的 table_bbox（扩展边界）
-        if table_bbox and len(table_bbox) == 4:
-            x1, y1, x2, y2 = table_bbox
-            
-            # 扩展边界（考虑边框外的文本）
-            margin = 20
-            expanded_bbox = [
-                max(0, x1 - margin),
-                max(0, y1 - margin),
-                x2 + margin,
-                y2 + margin
-            ]
-            
-            filtered = []
-            for box in paddle_boxes:
-                bbox = box['bbox']
-                box_center_x = (bbox[0] + bbox[2]) / 2
-                box_center_y = (bbox[1] + bbox[3]) / 2
-                
-                # 中心点在扩展区域内
-                if (expanded_bbox[0] <= box_center_x <= expanded_bbox[2] and
-                    expanded_bbox[1] <= box_center_y <= expanded_bbox[3]):
-                    filtered.append(box)
-            
-            if filtered:
-                # 计算实际边界框
-                actual_bbox = [
-                    min(b['bbox'][0] for b in filtered),
-                    min(b['bbox'][1] for b in filtered),
-                    max(b['bbox'][2] for b in filtered),
-                    max(b['bbox'][3] for b in filtered)
-                ]
-                return filtered, actual_bbox
-        
-        # 🎯 策略 2: 通过内容匹配推断区域
-        print("   ℹ️ 无 table_bbox，使用内容匹配推断表格区域...")
-        
-        # 提取 HTML 中的所有文本
-        from bs4 import BeautifulSoup
-        soup = BeautifulSoup(html, 'html.parser')
-        html_texts = set()
-        for cell in soup.find_all(['td', 'th']):
-            text = cell.get_text(strip=True)
-            if text:
-                html_texts.add(self.text_matcher.normalize_text(text))
-        
-        if not html_texts:
-            return [], [0, 0, 0, 0]
-        
-        # 找出与 HTML 内容匹配的 boxes
-        matched_boxes = []
-        for box in paddle_boxes:
-            normalized_text = self.text_matcher.normalize_text(box['text'])
-            
-            # 检查是否匹配
-            if any(normalized_text in ht or ht in normalized_text 
-                   for ht in html_texts):
-                matched_boxes.append(box)
-        
-        if not matched_boxes:
-            # 🔑 降级：如果精确匹配失败，使用模糊匹配
-            print("   ℹ️ 精确匹配失败，尝试模糊匹配...")
-            
-            from fuzzywuzzy import fuzz
-            for box in paddle_boxes:
-                normalized_text = self.text_matcher.normalize_text(box['text'])
-                
-                for ht in html_texts:
-                    similarity = fuzz.partial_ratio(normalized_text, ht)
-                    if similarity >= 70:  # 降低阈值
-                        matched_boxes.append(box)
-                        break
-    
-        if matched_boxes:
-            # 计算边界框
-            actual_bbox = [
-                min(b['bbox'][0] for b in matched_boxes),
-                min(b['bbox'][1] for b in matched_boxes),
-                max(b['bbox'][2] for b in matched_boxes),
-                max(b['bbox'][3] for b in matched_boxes)
-            ]
-            
-            # 🔑 扩展边界，包含可能遗漏的文本
-            margin = 30
-            expanded_bbox = [
-                max(0, actual_bbox[0] - margin),
-                max(0, actual_bbox[1] - margin),
-                actual_bbox[2] + margin,
-                actual_bbox[3] + margin
-            ]
-            
-            # 重新筛选（包含边界上的文本）
-            final_filtered = []
-            for box in paddle_boxes:
-                bbox = box['bbox']
-                box_center_x = (bbox[0] + bbox[2]) / 2
-                box_center_y = (bbox[1] + bbox[3]) / 2
-                
-                if (expanded_bbox[0] <= box_center_x <= expanded_bbox[2] and
-                    expanded_bbox[1] <= box_center_y <= expanded_bbox[3]):
-                    final_filtered.append(box)
-            
-            return final_filtered, actual_bbox
-        
-        # 🔑 最后的降级：返回所有 boxes
-        print("   ⚠️ 无法确定表格区域，使用所有 paddle boxes")
-        if paddle_boxes:
-            actual_bbox = [
-                min(b['bbox'][0] for b in paddle_boxes),
-                min(b['bbox'][1] for b in paddle_boxes),
-                max(b['bbox'][2] for b in paddle_boxes),
-                max(b['bbox'][3] for b in paddle_boxes)
-            ]
-            return paddle_boxes, actual_bbox
-        
-        return [], [0, 0, 0, 0]
-
-    def _group_paddle_boxes_by_rows(self, paddle_boxes: List[Dict], 
-                                    y_tolerance: int = 20) -> List[Dict]:
-        """
-        将 paddle_text_boxes 按 y 坐标分组（聚类）
-        
-        Args:
-            paddle_boxes: Paddle OCR 文字框列表
-            y_tolerance: Y 坐标容忍度（像素）
-        
-        Returns:
-            分组列表，每组包含 {'y_center': float, 'boxes': List[Dict]}
-        """
-        if not paddle_boxes:
-            return []
-        
-        # 计算每个 box 的中心 y 坐标
-        boxes_with_y = []
-        for box in paddle_boxes:
-            bbox = box['bbox']
-            y_center = (bbox[1] + bbox[3]) / 2
-            boxes_with_y.append({
-                'y_center': y_center,
-                'box': box
-            })
-        
-        # 按 y 坐标排序
-        boxes_with_y.sort(key=lambda x: x['y_center'])
-        
-        # 聚类
-        groups = []
-        current_group = None
-        
-        for item in boxes_with_y:
-            if current_group is None:
-                # 开始新组
-                current_group = {
-                    'y_center': item['y_center'],
-                    'boxes': [item['box']]
-                }
-            else:
-                # 检查是否属于当前组
-                if abs(item['y_center'] - current_group['y_center']) <= y_tolerance:
-                    current_group['boxes'].append(item['box'])
-                    # 更新组的中心（使用平均值）
-                    current_group['y_center'] = sum(
-                        b['bbox'][1] + b['bbox'][3] for b in current_group['boxes']
-                    ) / (2 * len(current_group['boxes']))
-                else:
-                    # 保存当前组，开始新组
-                    groups.append(current_group)
-                    current_group = {
-                        'y_center': item['y_center'],
-                        'boxes': [item['box']]
-                    }
-        
-        # 添加最后一组
-        if current_group:
-            groups.append(current_group)
-        
-        return groups
-
-
-    def _match_html_rows_to_paddle_groups(self, html_rows: List, 
-                                      grouped_boxes: List[Dict]) -> Dict[int, List[int]]:
-        """
-        智能匹配 HTML 行与 paddle 分组（改进版：处理跨行文本）
-
-        策略：
-        1. 第一遍：基于内容精确匹配
-        2. 第二遍：将未使用的组合并到相邻已匹配的行
-        """
-        if not html_rows or not grouped_boxes:
-            return {}
-        
-        mapping = {}
-        
-        # 🎯 策略 1: 数量相等，简单 1:1 映射
-        if len(html_rows) == len(grouped_boxes):
-            for i in range(len(html_rows)):
-                mapping[i] = [i]
-            return mapping
-        
-        # 🎯 策略 2: 第一遍 - 基于内容精确匹配
-        used_groups = set()
-        
-        for row_idx, row in enumerate(html_rows):
-            row_texts = [cell.get_text(strip=True) for cell in row.find_all(['td', 'th'])]
-            row_texts = [t for t in row_texts if t]
-            
-            if not row_texts:
-                mapping[row_idx] = []
-                continue
-            
-            row_text_normalized = [self.text_matcher.normalize_text(t) for t in row_texts]
-            
-            # 查找最匹配的 paddle 组
-            best_groups = []
-            best_score = 0
-            
-            # 尝试匹配单个组
-            for group_idx, group in enumerate(grouped_boxes):
-                if group_idx in used_groups:
-                    continue
-                
-                group_texts = [self.text_matcher.normalize_text(b['text']) 
-                              for b in group['boxes'] if not b.get('used')]
-                
-                match_count = sum(1 for rt in row_text_normalized 
-                                if any(rt in gt or gt in rt for gt in group_texts))
-                
-                coverage = match_count / len(row_texts) if row_texts else 0
-                
-                if coverage > best_score:
-                    best_score = coverage
-                    best_groups = [group_idx]
-            
-            # 🔑 如果单组匹配度不高，尝试匹配多个连续组
-            if best_score < 0.5:
-                # 从当前位置向后查找
-                start_group = min([g for g in range(len(grouped_boxes)) if g not in used_groups], 
-                                default=0)
-                combined_texts = []
-                combined_groups = []
-                
-                for group_idx in range(start_group, min(start_group + 5, len(grouped_boxes))):
-                    if group_idx in used_groups:
-                        continue
-                    
-                    combined_groups.append(group_idx)
-                    combined_texts.extend([
-                        self.text_matcher.normalize_text(b['text'])
-                        for b in grouped_boxes[group_idx]['boxes']
-                        if not b.get('used')
-                    ])
-                    
-                    match_count = sum(1 for rt in row_text_normalized
-                                    if any(rt in gt or gt in rt for gt in combined_texts))
-                    coverage = match_count / len(row_texts) if row_texts else 0
-                    
-                    if coverage > best_score:
-                        best_score = coverage
-                        best_groups = combined_groups.copy()
-        
-            # 记录映射
-            if best_groups and best_score > 0.3:
-                mapping[row_idx] = best_groups
-                used_groups.update(best_groups)
-            else:
-                # 降级策略：位置推测
-                estimated_group = min(row_idx, len(grouped_boxes) - 1)
-                if estimated_group not in used_groups:
-                    mapping[row_idx] = [estimated_group]
-                    used_groups.add(estimated_group)
-                else:
-                    mapping[row_idx] = []
-        
-        # 🎯 策略 3: 第二遍 - 处理未使用的组（关键！）
-        unused_groups = [i for i in range(len(grouped_boxes)) if i not in used_groups]
-        
-        if unused_groups:
-            print(f"   ℹ️ 发现 {len(unused_groups)} 个未匹配的 paddle 组: {unused_groups}")
-            
-            # 🔑 将未使用的组合并到相邻的已匹配行
-            for unused_idx in unused_groups:
-                # 🎯 关键改进：计算与相邻行的边界距离
-                unused_group = grouped_boxes[unused_idx]
-                unused_y_min = min(b['bbox'][1] for b in unused_group['boxes'])
-                unused_y_max = max(b['bbox'][3] for b in unused_group['boxes'])
-                
-                # 🔑 查找上方和下方最近的已使用组
-                above_idx = None
-                below_idx = None
-                above_distance = float('inf')
-                below_distance = float('inf')
-                
-                # 向上查找
-                for i in range(unused_idx - 1, -1, -1):
-                    if i in used_groups:
-                        above_idx = i
-                        # 🎯 边界距离：unused 的最小 y - above 的最大 y
-                        above_group = grouped_boxes[i]
-                        max_y_box = max(
-                            above_group['boxes'],
-                            key=lambda b: b['bbox'][3]
-                        )
-                        above_y_center = (max_y_box['bbox'][1] + max_y_box['bbox'][3]) / 2
-                        above_distance = abs(unused_y_min - above_y_center)
-                        print(f"      • 组 {unused_idx} 与上方组 {i} 距离: {above_distance:.1f}px")
-                        break
-                
-                # 向下查找
-                for i in range(unused_idx + 1, len(grouped_boxes)):
-                    if i in used_groups:
-                        below_idx = i
-                        # 🎯 边界距离：below 的最小 y - unused 的最大 y
-                        below_group = grouped_boxes[i]
-                        min_y_box = min(
-                            below_group['boxes'],
-                            key=lambda b: b['bbox'][1]
-                        )
-                        below_y_center = (min_y_box['bbox'][1] + min_y_box['bbox'][3]) / 2
-                        below_distance = abs(below_y_center - unused_y_max)
-                        print(f"      • 组 {unused_idx} 与下方组 {i} 距离: {below_distance:.1f}px")
-                        break
-                
-                # 🎯 选择距离更近的一侧
-                if above_idx is not None and below_idx is not None:
-                    # 都存在，选择距离更近的
-                    if above_distance < below_distance:
-                        closest_used_idx = above_idx
-                        merge_direction = "上方"
-                    else:
-                        closest_used_idx = below_idx
-                        merge_direction = "下方"
-                    print(f"      ✓ 组 {unused_idx} 选择合并到{merge_direction}组 {closest_used_idx}")
-                elif above_idx is not None:
-                    closest_used_idx = above_idx
-                    merge_direction = "上方"
-                elif below_idx is not None:
-                    closest_used_idx = below_idx
-                    merge_direction = "下方"
-                else:
-                    print(f"      ⚠️ 组 {unused_idx} 无相邻已使用组，跳过")
-                    continue
-                
-                # 🔑 找到该组对应的 HTML 行
-                target_html_row = None
-                for html_row_idx, group_indices in mapping.items():
-                    if closest_used_idx in group_indices:
-                        target_html_row = html_row_idx
-                        break
-                
-                if target_html_row is not None:
-                    # 🎯 根据合并方向决定目标行
-                    if merge_direction == "上方":
-                        # 合并到上方对应的 HTML 行
-                        if target_html_row in mapping:
-                            if unused_idx not in mapping[target_html_row]:
-                                mapping[target_html_row].append(unused_idx)
-                                print(f"      • 组 {unused_idx} 合并到 HTML 行 {target_html_row}（上方行）")
-                    else:
-                        # 合并到下方对应的 HTML 行
-                        if target_html_row in mapping:
-                            if unused_idx not in mapping[target_html_row]:
-                                mapping[target_html_row].append(unused_idx)
-                                print(f"      • 组 {unused_idx} 合并到 HTML 行 {target_html_row}（下方行）")
-                
-                used_groups.add(unused_idx)
-        
-        # 🔑 策略 4: 第三遍 - 按 y 坐标排序每行的组索引
-        for row_idx in mapping:
-            if mapping[row_idx]:
-                mapping[row_idx].sort(key=lambda idx: grouped_boxes[idx]['y_center'])
-        
-        return mapping
-
-    def _match_cell_sequential(self, cell_text: str, 
-                            boxes: List[Dict],
-                            col_boundaries: List[Tuple[int, int]],
-                            start_idx: int) -> Optional[Dict]:
-        """
-        🎯 顺序匹配单元格：从指定位置开始，逐步合并 boxes 直到匹配
-        
-        策略：
-        1. 找到第一个未使用的 box
-        2. 尝试单个 box 精确匹配
-        3. 如果失败，尝试合并多个 boxes
-        
-        Args:
-            cell_text: HTML 单元格文本
-            boxes: 候选 boxes（已按 x 坐标排序）
-            col_boundaries: 列边界列表
-            start_idx: 起始索引
-        
-        Returns:
-            {'bbox': [x1,y1,x2,y2], 'text': str, 'score': float, 
-            'paddle_indices': [idx1, idx2], 'used_boxes': [box1, box2],
-            'last_used_index': int}
-        """
-        from fuzzywuzzy import fuzz
-        
-        cell_text_normalized = self.text_matcher.normalize_text(cell_text)
-        
-        if len(cell_text_normalized) < 2:
-            return None
-        
-        # 🔑 找到第一个未使用的 box
-        first_unused_idx = start_idx
-        while first_unused_idx < len(boxes) and boxes[first_unused_idx].get('used'):
-            first_unused_idx += 1
-        
-        if first_unused_idx >= len(boxes):
-            return None
-
-        # 🔑 策略 1: 单个 box 精确匹配
-        for box in boxes[first_unused_idx:]:
-            if box.get('used'):
-                continue
-            
-            box_text = self.text_matcher.normalize_text(box['text'])
-            
-            if cell_text_normalized == box_text:
-                return self._build_match_result([box], box['text'], 100.0, boxes.index(box))
-        
-        # 🔑 策略 2: 多个 boxes 合并匹配
-        unused_boxes = [b for b in boxes if not b.get('used')]
-        # 合并同列的 boxes 合并
-        merged_bboxes = []
-        for col_idx in range(len(col_boundaries)):
-            combo_boxes = self._get_boxes_in_column(unused_boxes, col_boundaries, col_idx)
-            if len(combo_boxes) > 0:
-                sorted_combo = sorted(combo_boxes, key=lambda b: (b['bbox'][1], b['bbox'][0]))
-                merged_text = ''.join([b['text'] for b in sorted_combo])
-                merged_bboxes.append({
-                    'text': merged_text,
-                    'sorted_combo': sorted_combo
-                })
-
-        for box in merged_bboxes:
-            # 1. 精确匹配
-            merged_text_normalized = self.text_matcher.normalize_text(box['text'])
-            if cell_text_normalized == merged_text_normalized:
-                last_sort_idx = boxes.index(box['sorted_combo'][-1])
-                return self._build_match_result(box['sorted_combo'], box['text'], 100.0, last_sort_idx)
-            
-            # 2. 子串匹配
-            is_substring = (cell_text_normalized in merged_text_normalized or 
-                        merged_text_normalized in cell_text_normalized)
-            
-            # 3. 模糊匹配
-            similarity = fuzz.partial_ratio(cell_text_normalized, merged_text_normalized)
-            
-            # 🎯 子串匹配加分
-            if is_substring:
-                similarity = min(100, similarity + 10)
-            
-            if similarity >= self.text_matcher.similarity_threshold:
-                print(f"         ✓ 匹配成功: '{cell_text[:15]}' vs '{merged_text[:15]}' (相似度: {similarity})")
-                return self._build_match_result(box['sorted_combo'], box['text'], similarity, start_idx)
-        
-        print(f"         ✗ 匹配失败: '{cell_text[:15]}'")
-        return None
-
-
-    def _build_match_result(self, boxes: List[Dict], text: str, 
-                        score: float, last_index: int) -> Dict:
-        """构建匹配结果"""
-        merged_bbox = [
-            min(b['bbox'][0] for b in boxes),
-            min(b['bbox'][1] for b in boxes),
-            max(b['bbox'][2] for b in boxes),
-            max(b['bbox'][3] for b in boxes)
-        ]
-        
-        return {
-            'bbox': merged_bbox,
-            'text': text,
-            'score': score,
-            'paddle_indices': [b['paddle_bbox_index'] for b in boxes],
-            'used_boxes': boxes,
-            'last_used_index': last_index
-        }

merger/table_cell_matcher.py

@@ -0,0 +1,893 @@
+"""
+表格单元格匹配器
+负责将 HTML 表格单元格与 PaddleOCR bbox 进行匹配
+"""
+from typing import List, Dict, Tuple, Optional
+from bs4 import BeautifulSoup
+
+try:
+    from .text_matcher import TextMatcher
+except ImportError:
+    from text_matcher import TextMatcher
+
+
+class TableCellMatcher:
+    """表格单元格匹配器"""
+    
+    def __init__(self, text_matcher: TextMatcher, 
+                 x_tolerance: int = 3, 
+                 y_tolerance: int = 10):
+        """
+        Args:
+            text_matcher: 文本匹配器
+            x_tolerance: X轴容差（用于列边界判断）
+            y_tolerance: Y轴容差（用于行分组）
+        """
+        self.text_matcher = text_matcher
+        self.x_tolerance = x_tolerance
+        self.y_tolerance = y_tolerance
+    
+    def enhance_table_html_with_bbox(self, html: str, paddle_text_boxes: List[Dict],
+                                  start_pointer: int, table_bbox: Optional[List[int]] = None) -> Tuple[str, List[Dict], int]:
+        """
+        为 HTML 表格添加 bbox 信息（优化版：先筛选表格区域）
+        
+        策略：
+        1. 根据 table_bbox 筛选出表格区域内的 paddle_text_boxes
+        2. 将筛选后的 boxes 按行分组
+        3. 智能匹配 HTML 行与 paddle 行组
+        4. 在匹配的组内查找单元格
+    
+        Args:
+            html: HTML 表格
+            paddle_text_boxes: 全部 paddle OCR 结果
+            start_pointer: 开始位置
+            table_bbox: 表格边界框 [x1, y1, x2, y2]
+        """
+        soup = BeautifulSoup(html, 'html.parser')
+        cells = []
+        
+        # 🔑 第一步：筛选表格区域内的 paddle boxes
+        table_region_boxes, actual_table_bbox = self._filter_boxes_in_table_region(
+            paddle_text_boxes[start_pointer:],
+            table_bbox,
+            html
+        )
+        
+        if not table_region_boxes:
+            print(f"⚠️ 未在表格区域找到 paddle boxes")
+            return str(soup), cells, start_pointer
+        
+        print(f"📊 表格区域: {len(table_region_boxes)} 个文本框")
+        print(f"   边界: {actual_table_bbox}")
+        
+        # 🔑 第二步：将表格区域的 boxes 按行分组
+        grouped_boxes = self._group_paddle_boxes_by_rows(
+            table_region_boxes,
+            y_tolerance=self.y_tolerance
+        )
+        
+        # 🔑 第三步：在每组内按 x 坐标排序
+        for group in grouped_boxes:
+            group['boxes'].sort(key=lambda x: x['bbox'][0])
+        
+        grouped_boxes.sort(key=lambda g: g['y_center'])
+        
+        print(f"   分组: {len(grouped_boxes)} 行")
+        
+        # 🔑 第四步：智能匹配 HTML 行与 paddle 行组
+        html_rows = soup.find_all('tr')
+        row_mapping = self._match_html_rows_to_paddle_groups(html_rows, grouped_boxes)
+        
+        print(f"   HTML行: {len(html_rows)} 行")
+        print(f"   映射: {len([v for v in row_mapping.values() if v])} 个有效映射")
+        
+        # 🔑 第五步：遍历 HTML 表格，使用映射关系查找
+        for row_idx, row in enumerate(html_rows):
+            group_indices = row_mapping.get(row_idx, [])
+            
+            if not group_indices:
+                continue
+            
+            # 合并多个组的 boxes
+            current_boxes = []
+            for group_idx in group_indices:
+                if group_idx < len(grouped_boxes):
+                    current_boxes.extend(grouped_boxes[group_idx]['boxes'])
+            
+            current_boxes.sort(key=lambda x: x['bbox'][0])
+            
+            # 🎯 关键改进：提取 HTML 单元格并预先确定列边界
+            html_cells = row.find_all(['td', 'th'])
+            
+            if not html_cells:
+                continue
+            
+            # 🔑 预估列边界（基于 x 坐标分布）
+            col_boundaries = self._estimate_column_boundaries(
+                current_boxes, 
+                len(html_cells)
+            )
+            
+            print(f"   行 {row_idx + 1}: {len(html_cells)} 列，边界: {col_boundaries}")
+            
+            # 🎯 关键改进：顺序指针匹配
+            box_pointer = 0  # 当前行的 boxes 指针
+            
+            for col_idx, cell in enumerate(html_cells):
+                cell_text = cell.get_text(strip=True)
+                
+                if not cell_text:
+                    continue
+                
+                # 🔑 从当前指针开始匹配
+                matched_result = self._match_cell_sequential(
+                    cell_text,
+                    current_boxes,
+                    col_boundaries,
+                    box_pointer
+                )
+                
+                if matched_result:
+                    merged_bbox = matched_result['bbox']
+                    merged_text = matched_result['text']
+                    
+                    cell['data-bbox'] = f"[{merged_bbox[0]},{merged_bbox[1]},{merged_bbox[2]},{merged_bbox[3]}]"
+                    cell['data-score'] = f"{matched_result['score']:.4f}"
+                    cell['data-paddle-indices'] = str(matched_result['paddle_indices'])
+                    
+                    cells.append({
+                        'type': 'table_cell',
+                        'text': cell_text,
+                        'matched_text': merged_text,
+                        'bbox': merged_bbox,
+                        'row': row_idx + 1,
+                        'col': col_idx + 1,
+                        'score': matched_result['score'],
+                        'paddle_bbox_indices': matched_result['paddle_indices']
+                    })
+                    
+                    # 标记已使用
+                    for box in matched_result['used_boxes']:
+                        box['used'] = True
+                    
+                    # 🎯 移动指针到最后使用的 box 之后
+                    box_pointer = matched_result['last_used_index'] + 1
+                    
+                    print(f"      列 {col_idx + 1}: '{cell_text[:20]}...' 匹配 {len(matched_result['used_boxes'])} 个box (指针: {box_pointer})")
+        
+        # 计算新的指针位置
+        used_count = sum(1 for box in table_region_boxes if box.get('used'))
+        new_pointer = start_pointer + used_count
+        
+        print(f"   匹配: {len(cells)} 个单元格")
+        
+        return str(soup), cells, new_pointer
+
+
+    def _estimate_column_boundaries(self, boxes: List[Dict], 
+                                    num_cols: int) -> List[Tuple[int, int]]:
+        """
+        估算列边界（改进版：处理同列多文本框）
+        
+        Args:
+            boxes: 当前行的所有 boxes（已按 x 排序）
+            num_cols: HTML 表格的列数
+        
+        Returns:
+            列边界列表 [(x_start, x_end), ...]
+        """
+        if not boxes:
+            return []
+        
+        # 🔑 关键改进：先按 x 坐标聚类（合并同列的多个文本框）
+        x_clusters = self._cluster_boxes_by_x(boxes, x_tolerance=self.x_tolerance)
+        
+        print(f"      X聚类: {len(boxes)} 个boxes -> {len(x_clusters)} 个列簇")
+        
+        # 获取所有 x 坐标范围
+        x_min = min(cluster['x_min'] for cluster in x_clusters)
+        x_max = max(cluster['x_max'] for cluster in x_clusters)
+        
+        # 🎯 策略 1: 如果聚类数量<=列数接近
+        if len(x_clusters) <= num_cols:
+            # 直接使用聚类边界
+            boundaries = [(cluster['x_min'], cluster['x_max']) 
+                        for cluster in x_clusters]
+            return boundaries
+        
+        # 🎯 策略 2: 聚类数多于列数（某些列有多个文本簇）
+        if len(x_clusters) > num_cols:
+            print(f"      ℹ️ 聚类数 {len(x_clusters)} > 列数 {num_cols}，合并相近簇")
+            
+            # 合并相近的簇
+            merged_clusters = self._merge_close_clusters(x_clusters, num_cols)
+            
+            boundaries = [(cluster['x_min'], cluster['x_max']) 
+                        for cluster in merged_clusters]
+            return boundaries
+        
+        return []
+
+
+    def _cluster_boxes_by_x(self, boxes: List[Dict], 
+                    x_tolerance: int = 3) -> List[Dict]:
+        """
+        按 x 坐标聚类（合并同列的多个文本框）
+        
+        Args:
+            boxes: 文本框列表
+            x_tolerance: X坐标容忍度
+        
+        Returns:
+            聚类列表 [{'x_min': int, 'x_max': int, 'boxes': List[Dict]}, ...]
+        """
+        if not boxes:
+            return []
+        
+        # 按左边界 x 坐标排序
+        sorted_boxes = sorted(boxes, key=lambda b: b['bbox'][0])
+        
+        clusters = []
+        current_cluster = None
+        
+        for box in sorted_boxes:
+            bbox = box['bbox']
+            x_start = bbox[0]
+            x_end = bbox[2]
+            
+            if current_cluster is None:
+                # 开始新簇
+                current_cluster = {
+                    'x_min': x_start,
+                    'x_max': x_end,
+                    'boxes': [box]
+                }
+            else:
+                # 🔑 检查是否属于当前簇（修正后的逻辑）
+                # 1. x 坐标有重叠：x_start <= current_x_max 且 x_end >= current_x_min
+                # 2. 或者距离在容忍度内
+            
+                has_overlap = (x_start <= current_cluster['x_max'] and 
+                              x_end >= current_cluster['x_min'])
+            
+                is_close = abs(x_start - current_cluster['x_max']) <= x_tolerance
+            
+                if has_overlap or is_close:
+                    # 合并到当前簇
+                    current_cluster['boxes'].append(box)
+                    current_cluster['x_min'] = min(current_cluster['x_min'], x_start)
+                    current_cluster['x_max'] = max(current_cluster['x_max'], x_end)
+                else:
+                    # 保存当前簇，开始新簇
+                    clusters.append(current_cluster)
+                    current_cluster = {
+                        'x_min': x_start,
+                        'x_max': x_end,
+                        'boxes': [box]
+                    }
+    
+        # 添加最后一簇
+        if current_cluster:
+            clusters.append(current_cluster)
+        
+        return clusters
+
+
+    def _merge_close_clusters(self, clusters: List[Dict], 
+                            target_count: int) -> List[Dict]:
+        """
+        合并相近的簇，直到数量等于目标列数
+        
+        Args:
+            clusters: 聚类列表
+            target_count: 目标列数
+        
+        Returns:
+            合并后的聚类列表
+        """
+        if len(clusters) <= target_count:
+            return clusters
+        
+        # 复制一份，避免修改原数据
+        working_clusters = [c.copy() for c in clusters]
+        
+        while len(working_clusters) > target_count:
+            # 找到距离最近的两个簇
+            min_distance = float('inf')
+            merge_idx = 0
+            
+            for i in range(len(working_clusters) - 1):
+                distance = working_clusters[i + 1]['x_min'] - working_clusters[i]['x_max']
+                if distance < min_distance:
+                    min_distance = distance
+                    merge_idx = i
+            
+            # 合并
+            cluster1 = working_clusters[merge_idx]
+            cluster2 = working_clusters[merge_idx + 1]
+            
+            merged_cluster = {
+                'x_min': cluster1['x_min'],
+                'x_max': cluster2['x_max'],
+                'boxes': cluster1['boxes'] + cluster2['boxes']
+            }
+            
+            # 替换
+            working_clusters[merge_idx] = merged_cluster
+            working_clusters.pop(merge_idx + 1)
+        
+        return working_clusters
+
+
+    def _get_boxes_in_column(self, boxes: List[Dict], 
+                            boundaries: List[Tuple[int, int]],
+                            col_idx: int) -> List[Dict]:
+        """
+        获取指定列范围内的 boxes（改进版：包含重叠）
+        
+        Args:
+            boxes: 当前行的所有 boxes
+            boundaries: 列边界
+            col_idx: 列索引
+        
+        Returns:
+            该列的 boxes
+        """
+        if col_idx >= len(boundaries):
+            return []
+        
+        x_start, x_end = boundaries[col_idx]
+        
+        col_boxes = []
+        for box in boxes:
+            bbox = box['bbox']
+            box_x_start = bbox[0]
+            box_x_end = bbox[2]
+            
+            # 🔑 改进：检查是否有重叠（不只是中心点）
+            overlap = not (box_x_start > x_end or box_x_end < x_start)
+            
+            if overlap:
+                col_boxes.append(box)
+        
+        return col_boxes
+
+
+    def _filter_boxes_in_table_region(self, paddle_boxes: List[Dict],
+                                  table_bbox: Optional[List[int]],
+                                  html: str) -> Tuple[List[Dict], List[int]]:
+        """
+        筛选表格区域内的 paddle boxes
+    
+        策略：
+        1. 如果有 table_bbox，使用边界框筛选（扩展边界）
+        2. 如果没有 table_bbox，通过内容匹配推断区域
+    
+        Args:
+            paddle_boxes: paddle OCR 结果
+            table_bbox: 表格边界框 [x1, y1, x2, y2]
+            html: HTML 内容（用于内容验证）
+    
+        Returns:
+            (筛选后的 boxes, 实际表格边界框)
+        """
+        if not paddle_boxes:
+            return [], [0, 0, 0, 0]
+        
+        # 🎯 策略 1: 使用提供的 table_bbox（扩展边界）
+        if table_bbox and len(table_bbox) == 4:
+            x1, y1, x2, y2 = table_bbox
+            
+            # 扩展边界（考虑边框外的文本）
+            margin = 20
+            expanded_bbox = [
+                max(0, x1 - margin),
+                max(0, y1 - margin),
+                x2 + margin,
+                y2 + margin
+            ]
+            
+            filtered = []
+            for box in paddle_boxes:
+                bbox = box['bbox']
+                box_center_x = (bbox[0] + bbox[2]) / 2
+                box_center_y = (bbox[1] + bbox[3]) / 2
+                
+                # 中心点在扩展区域内
+                if (expanded_bbox[0] <= box_center_x <= expanded_bbox[2] and
+                    expanded_bbox[1] <= box_center_y <= expanded_bbox[3]):
+                    filtered.append(box)
+            
+            if filtered:
+                # 计算实际边界框
+                actual_bbox = [
+                    min(b['bbox'][0] for b in filtered),
+                    min(b['bbox'][1] for b in filtered),
+                    max(b['bbox'][2] for b in filtered),
+                    max(b['bbox'][3] for b in filtered)
+                ]
+                return filtered, actual_bbox
+        
+        # 🎯 策略 2: 通过内容匹配推断区域
+        print("   ℹ️ 无 table_bbox，使用内容匹配推断表格区域...")
+        
+        # 提取 HTML 中的所有文本
+        from bs4 import BeautifulSoup
+        soup = BeautifulSoup(html, 'html.parser')
+        html_texts = set()
+        for cell in soup.find_all(['td', 'th']):
+            text = cell.get_text(strip=True)
+            if text:
+                html_texts.add(self.text_matcher.normalize_text(text))
+        
+        if not html_texts:
+            return [], [0, 0, 0, 0]
+        
+        # 找出与 HTML 内容匹配的 boxes
+        matched_boxes = []
+        for box in paddle_boxes:
+            normalized_text = self.text_matcher.normalize_text(box['text'])
+            
+            # 检查是否匹配
+            if any(normalized_text in ht or ht in normalized_text 
+                   for ht in html_texts):
+                matched_boxes.append(box)
+        
+        if not matched_boxes:
+            # 🔑 降级：如果精确匹配失败，使用模糊匹配
+            print("   ℹ️ 精确匹配失败，尝试模糊匹配...")
+            
+            from fuzzywuzzy import fuzz
+            for box in paddle_boxes:
+                normalized_text = self.text_matcher.normalize_text(box['text'])
+                
+                for ht in html_texts:
+                    similarity = fuzz.partial_ratio(normalized_text, ht)
+                    if similarity >= 70:  # 降低阈值
+                        matched_boxes.append(box)
+                        break
+    
+        if matched_boxes:
+            # 计算边界框
+            actual_bbox = [
+                min(b['bbox'][0] for b in matched_boxes),
+                min(b['bbox'][1] for b in matched_boxes),
+                max(b['bbox'][2] for b in matched_boxes),
+                max(b['bbox'][3] for b in matched_boxes)
+            ]
+            
+            # 🔑 扩展边界，包含可能遗漏的文本
+            margin = 30
+            expanded_bbox = [
+                max(0, actual_bbox[0] - margin),
+                max(0, actual_bbox[1] - margin),
+                actual_bbox[2] + margin,
+                actual_bbox[3] + margin
+            ]
+            
+            # 重新筛选（包含边界上的文本）
+            final_filtered = []
+            for box in paddle_boxes:
+                bbox = box['bbox']
+                box_center_x = (bbox[0] + bbox[2]) / 2
+                box_center_y = (bbox[1] + bbox[3]) / 2
+                
+                if (expanded_bbox[0] <= box_center_x <= expanded_bbox[2] and
+                    expanded_bbox[1] <= box_center_y <= expanded_bbox[3]):
+                    final_filtered.append(box)
+            
+            return final_filtered, actual_bbox
+        
+        # 🔑 最后的降级：返回所有 boxes
+        print("   ⚠️ 无法确定表格区域，使用所有 paddle boxes")
+        if paddle_boxes:
+            actual_bbox = [
+                min(b['bbox'][0] for b in paddle_boxes),
+                min(b['bbox'][1] for b in paddle_boxes),
+                max(b['bbox'][2] for b in paddle_boxes),
+                max(b['bbox'][3] for b in paddle_boxes)
+            ]
+            return paddle_boxes, actual_bbox
+        
+        return [], [0, 0, 0, 0]
+
+    def _group_paddle_boxes_by_rows(self, paddle_boxes: List[Dict], 
+                                    y_tolerance: int = 10) -> List[Dict]:
+        """
+        将 paddle_text_boxes 按 y 坐标分组（聚类）
+        
+        Args:
+            paddle_boxes: Paddle OCR 文字框列表
+            y_tolerance: Y 坐标容忍度（像素）
+        
+        Returns:
+            分组列表，每组包含 {'y_center': float, 'boxes': List[Dict]}
+        """
+        if not paddle_boxes:
+            return []
+        
+        # 计算每个 box 的中心 y 坐标
+        boxes_with_y = []
+        for box in paddle_boxes:
+            bbox = box['bbox']
+            y_center = (bbox[1] + bbox[3]) / 2
+            boxes_with_y.append({
+                'y_center': y_center,
+                'box': box
+            })
+        
+        # 按 y 坐标排序
+        boxes_with_y.sort(key=lambda x: x['y_center'])
+        
+        # 聚类
+        groups = []
+        current_group = None
+        
+        for item in boxes_with_y:
+            if current_group is None:
+                # 开始新组
+                current_group = {
+                    'y_center': item['y_center'],
+                    'boxes': [item['box']]
+                }
+            else:
+                # 检查是否属于当前组
+                if abs(item['y_center'] - current_group['y_center']) <= y_tolerance:
+                    current_group['boxes'].append(item['box'])
+                    # 更新组的中心（使用平均值）
+                    current_group['y_center'] = sum(
+                        b['bbox'][1] + b['bbox'][3] for b in current_group['boxes']
+                    ) / (2 * len(current_group['boxes']))
+                else:
+                    # 保存当前组，开始新组
+                    groups.append(current_group)
+                    current_group = {
+                        'y_center': item['y_center'],
+                        'boxes': [item['box']]
+                    }
+        
+        # 添加最后一组
+        if current_group:
+            groups.append(current_group)
+        
+        return groups
+
+
+    def _match_html_rows_to_paddle_groups(self, html_rows: List, 
+                                      grouped_boxes: List[Dict]) -> Dict[int, List[int]]:
+        """
+        智能匹配 HTML 行与 paddle 分组（改进版：更激进的多组合并）
+
+        策略：
+        1. 第一遍：基于内容精确匹配（允许匹配多个连续组）
+        2. 第二遍：将未使用的组合并到相邻已匹配的行
+        """
+        if not html_rows or not grouped_boxes:
+            return {}
+        
+        mapping = {}
+        
+        # 🎯 策略 1: 数量相等，简单 1:1 映射
+        if len(html_rows) == len(grouped_boxes):
+            for i in range(len(html_rows)):
+                mapping[i] = grouped_boxes[i]
+            return mapping
+        
+        # 🎯 策略 2: 第一遍 - 基于内容精确匹配（使用预处理后的组）
+        used_groups = set()
+        
+        for row_idx, row in enumerate(html_rows):
+            row_texts = [cell.get_text(strip=True) for cell in row.find_all(['td', 'th'])]
+            row_texts = [t for t in row_texts if t]
+            
+            if not row_texts:
+                mapping[row_idx] = []
+                continue
+            
+            row_text_normalized = [self.text_matcher.normalize_text(t) for t in row_texts]
+            
+            # 🔑 关键改进：从当前位置开始，尝试匹配多个连续的**预处理组**
+            best_groups = []
+            best_score = 0
+            
+            # 找到第一个未使用的组
+            start_group = next(
+                (i for i in range(len(grouped_boxes)) if i not in used_groups),
+                None
+            )
+            if start_group is None:
+                mapping[row_idx] = []
+                continue
+            
+            max_window = 5
+            for group_count in range(1, max_window + 1):
+                end_group = start_group + group_count
+                if end_group > len(grouped_boxes):
+                    break
+
+                combined_group_indices = list(range(start_group, end_group))
+                if any(idx in used_groups for idx in combined_group_indices):
+                    continue
+
+                combined_texts = []
+                for g_idx in combined_group_indices:
+                    group_boxes = grouped_boxes[g_idx].get('boxes', [])
+                    for box in group_boxes:
+                        if box.get('used'):
+                            continue
+                        normalized_text = self.text_matcher.normalize_text(box.get('text', ''))
+                        if normalized_text:
+                            combined_texts.append(normalized_text)
+
+                if not combined_texts:
+                    continue
+                
+                # 计算匹配度
+                match_count = sum(1 for rt in row_text_normalized
+                                if any(rt in ct or ct in rt for ct in combined_texts))
+                coverage = match_count / len(row_texts) if row_texts else 0
+                
+                if coverage > best_score:
+                    best_score = coverage
+                    best_groups = combined_group_indices
+                    if coverage == 1.0:
+                        break  # 完美匹配，提前退出
+            
+            # 记录映射
+            if best_groups and best_score > 0.3:
+                mapping[row_idx] = best_groups
+                used_groups.update(best_groups)
+                print(f"   行 {row_idx}: 匹配组 {best_groups} (覆盖率: {best_score:.2f})")
+            else:
+                mapping[row_idx] = []       
+
+        # 🎯 策略 3: 第二遍 - 处理未使用的组（关键！）
+        unused_groups = [i for i in range(len(grouped_boxes)) if i not in used_groups]
+        
+        if unused_groups:
+            print(f"   ℹ️ 发现 {len(unused_groups)} 个未匹配的 paddle 组: {unused_groups}")
+            
+            # 🔑 将未使用的组合并到相邻的已匹配行
+            for unused_idx in unused_groups:
+                # 🎯 关键改进：计算与相邻行的边界距离
+                unused_group = grouped_boxes[unused_idx]
+                unused_y_min = min(b['bbox'][1] for b in unused_group['boxes'])
+                unused_y_max = max(b['bbox'][3] for b in unused_group['boxes'])
+                
+                # 🔑 查找上方和下方最近的已使用组
+                above_idx = None
+                below_idx = None
+                above_distance = float('inf')
+                below_distance = float('inf')
+                
+                # 向上查找
+                for i in range(unused_idx - 1, -1, -1):
+                    if i in used_groups:
+                        above_idx = i
+                        # 🎯 边界距离：unused 的最小 y - above 的最大 y
+                        above_group = grouped_boxes[i]
+                        max_y_box = max(
+                            above_group['boxes'],
+                            key=lambda b: b['bbox'][3]
+                        )
+                        above_y_center = (max_y_box['bbox'][1] + max_y_box['bbox'][3]) / 2
+                        above_distance = abs(unused_y_min - above_y_center)
+                        print(f"      • 组 {unused_idx} 与上方组 {i} 距离: {above_distance:.1f}px")
+                        break
+                
+                # 向下查找
+                for i in range(unused_idx + 1, len(grouped_boxes)):
+                    if i in used_groups:
+                        below_idx = i
+                        # 🎯 边界距离：below 的最小 y - unused 的最大 y
+                        below_group = grouped_boxes[i]
+                        min_y_box = min(
+                            below_group['boxes'],
+                            key=lambda b: b['bbox'][1]
+                        )
+                        below_y_center = (min_y_box['bbox'][1] + min_y_box['bbox'][3]) / 2
+                        below_distance = abs(below_y_center - unused_y_max)
+                        print(f"      • 组 {unused_idx} 与下方组 {i} 距离: {below_distance:.1f}px")
+                        break
+                
+                # 🎯 选择距离更近的一侧
+                if above_idx is not None and below_idx is not None:
+                    # 都存在，选择距离更近的
+                    if above_distance < below_distance:
+                        closest_used_idx = above_idx
+                        merge_direction = "上方"
+                    else:
+                        closest_used_idx = below_idx
+                        merge_direction = "下方"
+                    print(f"      ✓ 组 {unused_idx} 选择合并到{merge_direction}组 {closest_used_idx}")
+                elif above_idx is not None:
+                    closest_used_idx = above_idx
+                    merge_direction = "上方"
+                elif below_idx is not None:
+                    closest_used_idx = below_idx
+                    merge_direction = "下方"
+                else:
+                    print(f"      ⚠️ 组 {unused_idx} 无相邻已使用组，跳过")
+                    continue
+                
+                # 🔑 找到该组对应的 HTML 行
+                target_html_row = None
+                for html_row_idx, group_indices in mapping.items():
+                    if closest_used_idx in group_indices:
+                        target_html_row = html_row_idx
+                        break
+                
+                if target_html_row is not None:
+                    # 🎯 根据合并方向决定目标行
+                    if merge_direction == "上方":
+                        # 合并到上方对应的 HTML 行
+                        if target_html_row in mapping:
+                            if unused_idx not in mapping[target_html_row]:
+                                mapping[target_html_row].append(unused_idx)
+                                print(f"      • 组 {unused_idx} 合并到 HTML 行 {target_html_row}（上方行）")
+                    else:
+                        # 合并到下方对应的 HTML 行
+                        if target_html_row in mapping:
+                            if unused_idx not in mapping[target_html_row]:
+                                mapping[target_html_row].append(unused_idx)
+                                print(f"      • 组 {unused_idx} 合并到 HTML 行 {target_html_row}（下方行）")
+                
+                used_groups.add(unused_idx)
+        
+        # 🔑 策略 4: 第三遍 - 按 y 坐标排序每行的组索引
+        for row_idx in mapping:
+            if mapping[row_idx]:
+                mapping[row_idx].sort(key=lambda idx: grouped_boxes[idx]['y_center'])
+        
+        return mapping
+
+    def _preprocess_close_groups(self, grouped_boxes: List[Dict], 
+                                y_gap_threshold: int = 10) -> List[List[int]]:
+        """
+        🆕 预处理：将 y 间距很小的组预合并
+        
+        Args:
+            grouped_boxes: 原始分组
+            y_gap_threshold: Y 间距阈值（小于此值认为是同一行）
+        
+        Returns:
+            预处理后的组索引列表 [[0,1], [2], [3,4,5], ...]
+        """
+        if not grouped_boxes:
+            return []
+        
+        preprocessed = []
+        current_group = [0]
+        
+        for i in range(1, len(grouped_boxes)):
+            prev_group = grouped_boxes[i - 1]
+            curr_group = grouped_boxes[i]
+            
+            # 计算间距
+            prev_y_max = max(b['bbox'][3] for b in prev_group['boxes'])
+            curr_y_min = min(b['bbox'][1] for b in curr_group['boxes'])
+            
+            gap = abs(curr_y_min - prev_y_max)
+            
+            if gap <= y_gap_threshold:
+                # 间距很小，合并
+                current_group.append(i)
+                print(f"   预合并: 组 {i-1} 和 {i} (间距: {gap}px)")
+            else:
+                # 间距较大，开始新组
+                preprocessed.append(current_group)
+                current_group = [i]
+        
+        # 添加最后一组
+        if current_group:
+            preprocessed.append(current_group)
+        
+        return preprocessed
+
+    def _match_cell_sequential(self, cell_text: str, 
+                            boxes: List[Dict],
+                            col_boundaries: List[Tuple[int, int]],
+                            start_idx: int) -> Optional[Dict]:
+        """
+        🎯 顺序匹配单元格：从指定位置开始，逐步合并 boxes 直到匹配
+        
+        策略：
+        1. 找到第一个未使用的 box
+        2. 尝试单个 box 精确匹配
+        3. 如果失败，尝试合并多个 boxes
+        
+        Args:
+            cell_text: HTML 单元格文本
+            boxes: 候选 boxes（已按 x 坐标排序）
+            col_boundaries: 列边界列表
+            start_idx: 起始索引
+        
+        Returns:
+            {'bbox': [x1,y1,x2,y2], 'text': str, 'score': float, 
+            'paddle_indices': [idx1, idx2], 'used_boxes': [box1, box2],
+            'last_used_index': int}
+        """
+        from fuzzywuzzy import fuzz
+        
+        cell_text_normalized = self.text_matcher.normalize_text(cell_text)
+        
+        if len(cell_text_normalized) < 2:
+            return None
+        
+        # 🔑 找到第一个未使用的 box
+        first_unused_idx = start_idx
+        while first_unused_idx < len(boxes) and boxes[first_unused_idx].get('used'):
+            first_unused_idx += 1
+        
+        if first_unused_idx >= len(boxes):
+            return None
+
+        # 🔑 策略 1: 单个 box 精确匹配
+        for box in boxes[first_unused_idx:]:
+            if box.get('used'):
+                continue
+            
+            box_text = self.text_matcher.normalize_text(box['text'])
+            
+            if cell_text_normalized == box_text:
+                return self._build_match_result([box], box['text'], 100.0, boxes.index(box))
+        
+        # 🔑 策略 2: 多个 boxes 合并匹配
+        unused_boxes = [b for b in boxes if not b.get('used')]
+        # 合并同列的 boxes 合并
+        merged_bboxes = []
+        for col_idx in range(len(col_boundaries)):
+            combo_boxes = self._get_boxes_in_column(unused_boxes, col_boundaries, col_idx)
+            if len(combo_boxes) > 0:
+                sorted_combo = sorted(combo_boxes, key=lambda b: (b['bbox'][1], b['bbox'][0]))
+                merged_text = ''.join([b['text'] for b in sorted_combo])
+                merged_bboxes.append({
+                    'text': merged_text,
+                    'sorted_combo': sorted_combo
+                })
+
+        for box in merged_bboxes:
+            # 1. 精确匹配
+            merged_text_normalized = self.text_matcher.normalize_text(box['text'])
+            if cell_text_normalized == merged_text_normalized:
+                last_sort_idx = boxes.index(box['sorted_combo'][-1])
+                return self._build_match_result(box['sorted_combo'], box['text'], 100.0, last_sort_idx)
+            
+            # 2. 子串匹配
+            is_substring = (cell_text_normalized in merged_text_normalized or 
+                        merged_text_normalized in cell_text_normalized)
+            
+            # 3. 模糊匹配
+            similarity = fuzz.partial_ratio(cell_text_normalized, merged_text_normalized)
+            
+            # 🎯 子串匹配加分
+            if is_substring:
+                similarity = min(100, similarity + 10)
+            
+            if similarity >= self.text_matcher.similarity_threshold:
+                print(f"         ✓ 匹配成功: '{cell_text[:15]}' vs '{merged_text[:15]}' (相似度: {similarity})")
+                return self._build_match_result(box['sorted_combo'], box['text'], similarity, start_idx)
+        
+        print(f"         ✗ 匹配失败: '{cell_text[:15]}'")
+        return None
+
+
+    def _build_match_result(self, boxes: List[Dict], text: str, 
+                        score: float, last_index: int) -> Dict:
+        """构建匹配结果"""
+        merged_bbox = [
+            min(b['bbox'][0] for b in boxes),
+            min(b['bbox'][1] for b in boxes),
+            max(b['bbox'][2] for b in boxes),
+            max(b['bbox'][3] for b in boxes)
+        ]
+        
+        return {
+            'bbox': merged_bbox,
+            'text': text,
+            'score': score,
+            'paddle_indices': [b['paddle_bbox_index'] for b in boxes],
+            'used_boxes': boxes,
+            'last_used_index': last_index
+        }