_match_html_rows_to_paddle_groups还需优化

merger/table_cell_matcher.py

@@ -4,6 +4,7 @@
 """
 from typing import List, Dict, Tuple, Optional
 from bs4 import BeautifulSoup
+import numpy as np
 
 try:
     from .text_matcher import TextMatcher
@@ -64,7 +65,8 @@ class TableCellMatcher:
         # 🔑 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
+            y_tolerance=self.y_tolerance,
+            auto_correct_skew=True
         )
         
         # 🔑 第三步：在每组内按 x 坐标排序
@@ -493,21 +495,36 @@ class TableCellMatcher:
         return [], [0, 0, 0, 0]
 
     def _group_paddle_boxes_by_rows(self, paddle_boxes: List[Dict], 
-                                    y_tolerance: int = 10) -> List[Dict]:
+                                    y_tolerance: int = 10,
+                                    auto_correct_skew: bool = True) -> List[Dict]:
         """
-        将 paddle_text_boxes 按 y 坐标分组（聚类）
-        
+        将 paddle_text_boxes 按 y 坐标分组（聚类）- 增强版本
+    
         Args:
             paddle_boxes: Paddle OCR 文字框列表
             y_tolerance: Y 坐标容忍度（像素）
-        
+            auto_correct_skew: 是否自动校正倾斜
+    
         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
+        # 🎯 步骤 1: 检测并校正倾斜
+        if auto_correct_skew:
+            rotation_angle = self._calculate_rotation_angle_from_polys(paddle_boxes)
+            
+            if abs(rotation_angle) > 0.5:  # 倾斜角度 > 0.5 度才校正
+                # 假设图像尺寸从第一个 box 估算
+                max_x = max(box['bbox'][2] for box in paddle_boxes)
+                max_y = max(box['bbox'][3] for box in paddle_boxes)
+                image_size = (max_x, max_y)
+                
+                print(f"   🔧 校正倾斜角度: {rotation_angle:.2f}°")
+                paddle_boxes = self._correct_bbox_skew(paddle_boxes, -rotation_angle, image_size)
+        
+        # 🎯 步骤 2: 按校正后的 y 坐标分组
         boxes_with_y = []
         for box in paddle_boxes:
             bbox = box['bbox']
@@ -520,10 +537,13 @@ class TableCellMatcher:
         # 按 y 坐标排序
         boxes_with_y.sort(key=lambda x: x['y_center'])
         
-        # 聚类
+        # 聚类（增强容忍度）
         groups = []
         current_group = None
         
+        # 🔑 动态调整容忍度（倾斜校正后可以更严格）
+        # effective_tolerance = y_tolerance if auto_correct_skew else y_tolerance * 1.5
+        
         for item in boxes_with_y:
             if current_group is None:
                 # 开始新组
@@ -532,36 +552,217 @@ class TableCellMatcher:
                     '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）
+                    # 更新组的中心
                     current_group['y_center'] = sum(
-                        b['bbox'][1] + b['bbox'][3] for b in current_group['boxes']
-                    ) / (2 * len(current_group['boxes']))
+                        (b['bbox'][1] + b['bbox'][3]) / 2 for b in current_group['boxes']
+                    ) / 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)
         
+        print(f"   ✓ 分组完成: {len(groups)} 行")
+        
         return groups
 
 
+    def _calculate_rotation_angle_from_polys(self, paddle_boxes: List[Dict], 
+                                            sample_ratio: float = 0.5,
+                                            outlier_threshold: float = 0.3) -> float:
+        """
+        从 dt_polys 计算文档倾斜角度（改进版：更鲁棒）
+        """
+        if not paddle_boxes:
+            return 0.0
+        
+        # 🎯 步骤1: 收集文本行的倾斜角度
+        line_angles = []
+        
+        for box in paddle_boxes:
+            poly = box.get('poly', [])
+            if len(poly) < 4:
+                continue
+            
+            # 提取上边缘的两个点
+            x1, y1 = poly[0]
+            x2, y2 = poly[1]
+            
+            # 计算宽度和高度
+            width = abs(x2 - x1)
+            height = abs(poly[2][1] - y1)
+            
+            # 🔑 过滤条件
+            if width < 50:  # 太短的文本不可靠
+                continue
+            
+            if width < height * 0.5:  # 垂直文本
+                continue
+            
+            # ⚠️ 关键修复：考虑图像坐标系（y 轴向下）
+            dx = x2 - x1
+            dy = y2 - y1
+            
+            if abs(dx) > 10:
+                # 🔧 使用 -arctan2 来校正坐标系方向
+                # 图像中向右下倾斜（dy>0）应该返回负角度
+                angle_rad = -np.arctan2(dy, dx)
+                
+                # 只保留小角度倾斜（-15° ~ +15°）
+                if abs(angle_rad) < np.radians(15):
+                    line_angles.append({
+                        'angle': angle_rad,
+                        'weight': width,  # 长文本行权重更高
+                        'y_center': (y1 + poly[2][1]) / 2
+                    })
+        
+        if len(line_angles) < 5:
+            print("   ⚠️ 有效样本不足，跳过倾斜校正")
+            return 0.0
+        
+        # 🎯 步骤2: 按 y 坐标排序，只使用中间区域
+        line_angles.sort(key=lambda x: x['y_center'])
+        
+        start_idx = int(len(line_angles) * (1 - sample_ratio) / 2)
+        end_idx = int(len(line_angles) * (1 + sample_ratio) / 2)
+        
+        sampled_angles = line_angles[start_idx:end_idx]
+        
+        # 🎯 步骤3: 计算中位数角度（初步估计）
+        raw_angles = [item['angle'] for item in sampled_angles]
+        median_angle = np.median(raw_angles)
+        
+        # 🎯 步骤4: 过滤异常值（与中位数差异过大）
+        filtered_angles = []
+        for item in sampled_angles:
+            if abs(item['angle'] - median_angle) < outlier_threshold:
+                filtered_angles.append(item)
+        
+        if len(filtered_angles) < 3:
+            print("   ⚠️ 过滤后样本不足")
+            return np.degrees(median_angle)
+        
+        # 🎯 步骤5: 加权平均（长文本行权重更高）
+        total_weight = sum(item['weight'] for item in filtered_angles)
+        weighted_angle = sum(
+            item['angle'] * item['weight'] for item in filtered_angles
+        ) / total_weight
+        
+        angle_deg = np.degrees(weighted_angle)
+        
+        print(f"   📐 倾斜角度检测:")
+        print(f"      • 原始样本: {len(line_angles)} 个")
+        print(f"      • 中间采样: {len(sampled_angles)} 个")
+        print(f"      • 过滤后: {len(filtered_angles)} 个")
+        print(f"      • 中位数角度: {np.degrees(median_angle):.3f}°")
+        print(f"      • 加权平均: {angle_deg:.3f}°")
+        
+        return angle_deg
+
+    def _rotate_point(self, point: Tuple[float, float], 
+                     angle_deg: float, 
+                     center: Tuple[float, float] = (0, 0)) -> Tuple[float, float]:
+        """
+        旋转点坐标
+    
+        Args:
+            point: 原始点 (x, y)
+            angle_deg: 旋转角度（度数，正值表示逆时针）
+            center: 旋转中心
+    
+        Returns:
+            旋转后的点 (x', y')
+        """
+        x, y = point
+        cx, cy = center
+        
+        # 转换为弧度
+        angle_rad = np.radians(angle_deg)
+        
+        # 平移到原点
+        x -= cx
+        y -= cy
+        
+        # 旋转
+        x_new = x * np.cos(angle_rad) - y * np.sin(angle_rad)
+        y_new = x * np.sin(angle_rad) + y * np.cos(angle_rad)
+        
+        # 平移回去
+        x_new += cx
+        y_new += cy
+        
+        return (x_new, y_new)
+
+
+    def _correct_bbox_skew(self, paddle_boxes: List[Dict], 
+                          rotation_angle: float,
+                          image_size: Tuple[int, int]) -> List[Dict]:
+        """
+        校正文本框的倾斜
+    
+        Args:
+            paddle_boxes: Paddle OCR 结果
+            rotation_angle: 倾斜角度
+            image_size: 图像尺寸 (width, height)
+    
+        Returns:
+            校正后的文本框列表
+        """
+        if abs(rotation_angle) < 0.1:  # 倾斜角度很小，不需要校正
+            return paddle_boxes
+        
+        width, height = image_size
+        center = (width / 2, height / 2)
+        
+        corrected_boxes = []
+        
+        for box in paddle_boxes:
+            poly = box.get('poly', [])
+            if len(poly) < 4:
+                corrected_boxes.append(box)
+                continue
+            
+            # 🎯 旋转多边形的四个角点
+            rotated_poly = [
+                self._rotate_point(point, -rotation_angle, center)
+                for point in poly
+            ]
+            
+            # 重新计算 bbox
+            x_coords = [p[0] for p in rotated_poly]
+            y_coords = [p[1] for p in rotated_poly]
+            
+            corrected_bbox = [
+                min(x_coords),
+                min(y_coords),
+                max(x_coords),
+                max(y_coords)
+            ]
+            
+            # 创建校正后的 box
+            corrected_box = box.copy()
+            corrected_box['bbox'] = corrected_bbox
+            corrected_box['poly'] = rotated_poly
+            corrected_box['original_bbox'] = box['bbox']  # 保存原始坐标
+            
+            corrected_boxes.append(corrected_box)
+        
+        return corrected_boxes
+
     def _match_html_rows_to_paddle_groups(self, html_rows: List, 
-                                      grouped_boxes: List[Dict]) -> Dict[int, List[int]]:
+                                        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）
+        智能匹配 HTML 行与 paddle 分组（修正版：严格顺序匹配）
 
         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
+        1. 数量相等：1:1 映射
+        2. 数量不等：按内容匹配，但保持 y 坐标顺序
         """
         if not html_rows or not grouped_boxes:
             return {}
@@ -574,8 +775,10 @@ class TableCellMatcher:
                 mapping[i] = [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）
+        # 🎯 策略 2: 基于内容匹配（修正版：严格单调递增）
+        from fuzzywuzzy import fuzz
         used_groups = set()
+        next_group_to_check = 0  # 🔑 关键改进：维护全局组索引
         
         for row_idx, row in enumerate(html_rows):
             row_texts = [cell.get_text(strip=True) for cell in row.find_all(['td', 'th'])]
@@ -586,30 +789,28 @@ class TableCellMatcher:
                 continue
             
             row_text_normalized = [self.text_matcher.normalize_text(t) for t in row_texts]
+            row_combined_text = ''.join(row_text_normalized)
             
-            # 🔑 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
-            
+            # 🔑 关键改进：从 next_group_to_check 开始搜索
             max_window = 5
             for group_count in range(1, max_window + 1):
+                # 🔑 从当前位置开始，而不是从第一个未使用的组
+                start_group = next_group_to_check
                 end_group = start_group + group_count
+                
                 if end_group > len(grouped_boxes):
                     break
 
                 combined_group_indices = list(range(start_group, end_group))
+                
+                # 🔑 跳过已使用的组（但不重新计算 start_group）
                 if any(idx in used_groups for idx in combined_group_indices):
                     continue
 
+                # 收集组内所有文本
                 combined_texts = []
                 for g_idx in combined_group_indices:
                     group_boxes = grouped_boxes[g_idx].get('boxes', [])
@@ -623,24 +824,75 @@ class TableCellMatcher:
                 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))
+                paddle_combined_text = ''.join(combined_texts)
+                
+                # 匹配策略
+                match_count = 0
+                
+                for rt in row_text_normalized:
+                    if len(rt) < 2:
+                        continue
+                    
+                    # 精确匹配
+                    if any(rt == ct for ct in combined_texts):
+                        match_count += 1
+                        continue
+                    
+                    # 子串匹配
+                    if any(rt in ct or ct in rt for ct in combined_texts):
+                        match_count += 1
+                        continue
+                    
+                    # 在合并文本中查找
+                    if rt in paddle_combined_text:
+                        match_count += 1
+                        continue
+                    
+                    # 模糊匹配
+                    for ct in combined_texts:
+                        similarity = fuzz.partial_ratio(rt, ct)
+                        if similarity >= 75:
+                            match_count += 1
+                            break
+                
+                # 整行匹配
+                row_similarity = fuzz.partial_ratio(row_combined_text, paddle_combined_text)
+                
                 coverage = match_count / len(row_texts) if row_texts else 0
+                combined_coverage = row_similarity / 100.0
+                
+                final_score = max(coverage, combined_coverage)
                 
-                if coverage > best_score:
-                    best_score = coverage
+                if final_score > best_score:
+                    best_score = final_score
                     best_groups = combined_group_indices
-                    if coverage == 1.0:
-                        break  # 完美匹配，提前退出
+                    
+                    print(f"   行 {row_idx} 候选: 组 {combined_group_indices}, "
+                        f"单元格匹配: {match_count}/{len(row_texts)}, "
+                        f"整行相似度: {row_similarity}%, "
+                        f"最终得分: {final_score:.2f}")
+                    
+                    if final_score >= 0.9:
+                        break
             
-            # 记录映射
-            if best_groups and best_score > 0.3:
+            # 🔑 降低阈值
+            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})")
+                
+                # 🔑 关键改进：更新下一个要检查的组
+                next_group_to_check = max(best_groups) + 1
+                
+                print(f"   ✓ 行 {row_idx}: 匹配组 {best_groups} (得分: {best_score:.2f}), "
+                    f"下次从组 {next_group_to_check} 开始")
             else:
-                mapping[row_idx] = []       
+                mapping[row_idx] = []
+                # 🔑 关键改进：即使没匹配，也要推进指针（假设跳过 1 个组）
+                if next_group_to_check < len(grouped_boxes):
+                    next_group_to_check += 1
+                
+                print(f"   ✗ 行 {row_idx}: 无匹配 (最佳得分: {best_score:.2f}), "
+                    f"推进到组 {next_group_to_check}")
 
         # 🎯 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]
@@ -875,16 +1127,23 @@ class TableCellMatcher:
 
     def _build_match_result(self, boxes: List[Dict], text: str, 
                         score: float, last_index: int) -> Dict:
-        """构建匹配结果"""
+        """构建匹配结果（使用原始坐标）"""
+    
+        # 🔑 关键修复：使用 original_bbox（如果存在）
+        def get_original_bbox(box: Dict) -> List[int]:
+            return box.get('original_bbox', box['bbox'])
+        
+        original_bboxes = [get_original_bbox(b) for b in boxes]
+        
         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)
+            min(b[0] for b in original_bboxes),
+            min(b[1] for b in original_bboxes),
+            max(b[2] for b in original_bboxes),
+            max(b[3] for b in original_bboxes)
         ]
         
         return {
-            'bbox': merged_bbox,
+            'bbox': merged_bbox,  # ✅ 使用原始坐标
             'text': text,
             'score': score,
             'paddle_indices': [b['paddle_bbox_index'] for b in boxes],

merger/table_cell_matcher_v1.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] = [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
+        }