ocr_verify/merger/table_cell_matcher.py

"""
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
        }