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
import numpy as np

try:
    from rapidfuzz import fuzz
except ImportError:
    from fuzzywuzzy import fuzz

try:
    from .text_matcher import TextMatcher
    from .bbox_extractor import BBoxExtractor
except ImportError:
    from text_matcher import TextMatcher
    from bbox_extractor import BBoxExtractor

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,
            auto_correct_skew=True
        )
        
        # 🔑 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...")
            
            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,
                                    auto_correct_skew: bool = True) -> List[Dict]:
        """
        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 []
        
        # 🎯 步骤 1: 检测并校正倾斜（使用 BBoxExtractor）
        if auto_correct_skew:
            rotation_angle = BBoxExtractor.calculate_skew_angle(paddle_boxes)
            
            if abs(rotation_angle) > 0.5:
                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 = BBoxExtractor.correct_boxes_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']
            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'])
        
        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:
                if abs(item['y_center'] - current_group['y_center']) <= y_tolerance:
                    current_group['boxes'].append(item['box'])
                    # 更新组的中心
                    current_group['y_center'] = sum(
                        (b['bbox'][1] + b['bbox'][3]) / 2 for b in current_group['boxes']
                    ) / len(current_group['boxes'])
                else:
                    groups.append(current_group)
                    current_group = {
                        'y_center': item['y_center'],
                        'boxes': [item['box']]
                    }
        
        if current_group:
            groups.append(current_group)
        
        print(f"   ✓ 分组完成: {len(groups)} 行")
        
        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（增强版 DP：支持跳过 HTML 行，防止链条断裂）
        """
        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
        
        # --- 准备数据 ---
        # 提取 HTML 文本
        html_row_texts = []
        for row in html_rows:
            cells = row.find_all(['td', 'th'])
            texts = [self.text_matcher.normalize_text(c.get_text(strip=True)) for c in cells]
            html_row_texts.append("".join(texts))

        # 预计算所有组的文本
        group_texts = []
        for group in grouped_boxes:
            boxes = group['boxes']
            texts = [self.text_matcher.normalize_text(b['text']) for b in boxes]
            group_texts.append("".join(texts))

        n_html = len(html_row_texts)
        n_paddle = len(grouped_boxes)

        # ⚡️ 优化 3: 预计算合并文本
        MAX_MERGE = 4
        merged_cache = {}
        for j in range(n_paddle):
            current_t = ""
            for k in range(MAX_MERGE):
                if j + k < n_paddle:
                    current_t += group_texts[j + k]
                    merged_cache[(j, k + 1)] = current_t
                else:
                    break

        # --- 动态规划 (DP) ---
        # dp[i][j] 表示：HTML 前 i 行 (0..i) 匹配到了 Paddle 的前 j 组 (0..j) 的最大得分
        # 初始化为负无穷
        dp = np.full((n_html, n_paddle), -np.inf)
        # 记录路径：path[i][j] = (prev_j, start_j) 
        # prev_j: 上一行结束的 paddle index
        # start_j: 当前行开始的 paddle index (因为一行可能对应多个组)
        path = {} 

        # 参数配置
        SEARCH_WINDOW = 15  # 向前搜索窗口
        SKIP_PADDLE_PENALTY = 0.1  # 跳过 Paddle 组的惩罚
        SKIP_HTML_PENALTY = 0.3    # 关键：跳过 HTML 行的惩罚        
        # --- 1. 初始化第一行 ---
        # 选项 A: 匹配 Paddle 组
        for end_j in range(min(n_paddle, SEARCH_WINDOW + MAX_MERGE)):
            for count in range(1, MAX_MERGE + 1):
                start_j = end_j - count + 1
                if start_j < 0: continue
                
                current_text = merged_cache.get((start_j, count), "")
                similarity = self._calculate_similarity(html_row_texts[0], current_text)
                
                penalty = start_j * SKIP_PADDLE_PENALTY
                score = similarity - penalty
                
                # 只有得分尚可才作为有效状态
                if score > 0.1:
                    if score > dp[0][end_j]:
                        dp[0][end_j] = score
                        path[(0, end_j)] = (-1, start_j)
        
        # 选项 B: 第一行就跳过 (虽然少见，但为了完整性)
        # 如果第一行跳过，相当于没有消耗任何 paddle 组，状态难以用 dp[0][j] 表达
        # 这里简化处理，假设第一行必须匹配点什么，或者由后续行修正

        # --- 2. 状态转移 ---
        for i in range(1, n_html):
            html_text = html_row_texts[i]
            
            # 获取上一行所有有效位置
            valid_prev_indices = [j for j in range(n_paddle) if dp[i-1][j] > -np.inf]
            
            # 剪枝
            if len(valid_prev_indices) > 30:
                valid_prev_indices.sort(key=lambda j: dp[i-1][j], reverse=True)
                valid_prev_indices = valid_prev_indices[:30]

            # 🛡️ 关键修复：允许跳过当前 HTML 行 (继承上一行的状态)
            # 如果跳过当前行，Paddle 指针 j 不变
            for prev_j in valid_prev_indices:
                score_skip = dp[i-1][prev_j] - SKIP_HTML_PENALTY
                if score_skip > dp[i][prev_j]:
                    dp[i][prev_j] = score_skip
                    # 记录路径：start_j = prev_j + 1 表示没有消耗新组 (空范围)
                    path[(i, prev_j)] = (prev_j, prev_j + 1)

            # 如果是空行，直接跳过计算，仅保留继承的状态
            if not html_text:
                continue

            # 正常匹配逻辑
            for prev_j in valid_prev_indices:
                prev_score = dp[i-1][prev_j]
                
                max_gap = min(SEARCH_WINDOW, n_paddle - prev_j - 1)
                
                for gap in range(max_gap):
                    start_j = prev_j + 1 + gap
                    
                    for count in range(1, MAX_MERGE + 1):
                        end_j = start_j + count - 1
                        if end_j >= n_paddle: break
                        
                        current_text = merged_cache.get((start_j, count), "")
                        
                        # 长度预筛选
                        h_len = len(html_text)
                        p_len = len(current_text)
                        if h_len > 10 and p_len < h_len * 0.2:
                            continue

                        similarity = self._calculate_similarity(html_text, current_text)
                        
                        # 计算惩罚
                        # 1. 跳过惩罚 (gap)
                        # 2. 长度惩罚 (防止过度合并)
                        len_penalty = 0.0
                        if h_len > 0:
                            ratio = p_len / h_len
                            if ratio > 2.0: len_penalty = (ratio - 2.0) * 0.2

                        current_score = similarity - (gap * SKIP_PADDLE_PENALTY) - len_penalty
                        
                        # 只有正收益才转移
                        if current_score > 0.1:
                            total_score = prev_score + current_score
                            
                            if total_score > dp[i][end_j]:
                                dp[i][end_j] = total_score
                                path[(i, end_j)] = (prev_j, start_j)

        # --- 3. 回溯找最优路径 ---
        # 找到最后一行得分最高的结束位置
        best_end_j = -1
        max_score = -np.inf
        
        # 优先找最后一行，如果最后一行没匹配上，往前找
        found_end = False
        for i in range(n_html - 1, -1, -1):
            for j in range(n_paddle):
                if dp[i][j] > max_score:
                    max_score = dp[i][j]
                    best_end_j = j
                    best_last_row = i
            if max_score > -np.inf:
                found_end = True
                break
        
        mapping = {}
        used_groups = set()
        
        if found_end:
            curr_i = best_last_row
            curr_j = best_end_j
            
            while curr_i >= 0:
                if (curr_i, curr_j) in path:
                    prev_j, start_j = path[(curr_i, curr_j)]
                    
                    # 如果 start_j <= curr_j，说明消耗了 Paddle 组
                    # 如果 start_j > curr_j，说明是跳过 HTML 行 (空范围)
                    if start_j <= curr_j:
                        indices = list(range(start_j, curr_j + 1))
                        mapping[curr_i] = indices
                        used_groups.update(indices)
                    else:
                        mapping[curr_i] = []
                    
                    curr_j = prev_j
                    curr_i -= 1
                else:
                    break
        
        # 填补未匹配的行
        for i in range(n_html):
            if i not in mapping:
                mapping[i] = []

        # --- 4. 后处理：未匹配组的归属 (Orphans) ---
        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}")
            for unused_idx in unused_groups:
                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'])
                
                above_idx = None
                below_idx = None
                above_distance = float('inf')
                below_distance = float('inf')
                
                for i in range(unused_idx - 1, -1, -1):
                    if i in used_groups:
                        above_idx = i
                        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)
                        break
                
                for i in range(unused_idx + 1, len(grouped_boxes)):
                    if i in used_groups:
                        below_idx = i
                        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)
                        break
                
                closest_used_idx = None
                merge_direction = ""
                
                if above_idx is not None and below_idx is not None:
                    if above_distance < below_distance:
                        closest_used_idx = above_idx
                        merge_direction = "上方"
                    else:
                        closest_used_idx = below_idx
                        merge_direction = "下方"
                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"
                
                if closest_used_idx is not None:
                    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:
                        if unused_idx not in mapping[target_html_row]:
                            mapping[target_html_row].append(unused_idx)
                            mapping[target_html_row].sort()
                            print(f"      • 组 {unused_idx} 合并到 HTML 行 {target_html_row}（{merge_direction}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 _calculate_similarity(self, text1: str, text2: str) -> float:
        """
        计算两个文本的相似度，结合字符覆盖率和序列相似度 (性能优化版)
        """
        if not text1 or not text2:
            return 0.0
            
        len1, len2 = len(text1), len(text2)
        
        # ⚡️ 优化 1: 长度快速检查
        # 如果长度差异过大（例如一个 50 字符，一个 2 字符），直接认为不匹配
        if len1 > 0 and len2 > 0:
            min_l, max_l = min(len1, len2), max(len1, len2)
            if max_l > 10 and min_l / max_l < 0.2:
                return 0.0

        # 1. 字符覆盖率 (Character Overlap)
        from collections import Counter
        c1 = Counter(text1)
        c2 = Counter(text2)
        
        intersection = c1 & c2
        overlap_count = sum(intersection.values())
        
        coverage = overlap_count / len1 if len1 > 0 else 0
        
        # ⚡️ 优化 2: 覆盖率低时跳过昂贵的 fuzz 计算
        # 如果字符重叠率低于 30%，说明内容基本不相关，没必要算序列相似度
        if coverage < 0.3:
            return coverage * 0.7

        # 2. 序列相似度 (Sequence Similarity)
        # 使用 token_sort_ratio 来容忍一定的乱序
        seq_score = fuzz.token_sort_ratio(text1, text2) / 100.0
        
        return (coverage * 0.7) + (seq_score * 0.3)

    def _preprocess_text_for_matching(self, text: str) -> str:
        """
        预处理文本：在不同类型的字符（如中文和数字/英文）之间插入空格，
        以便于 token_sort_ratio 更准确地进行分词和匹配。
        """
        if not text:
            return ""
        import re
        # 1. 在中文和非中文(数字/字母)之间插入空格
        # 例如: "2024年" -> "2024 年", "ID号码123" -> "ID号码 123"
        text = re.sub(r'([\u4e00-\u9fa5])([a-zA-Z0-9])', r'\1 \2', text)
        text = re.sub(r'([a-zA-Z0-9])([\u4e00-\u9fa5])', r'\1 \2', text)
        return text

    def _calculate_subsequence_score(self, target: str, source: str) -> float:
        """
        计算子序列匹配得分 (解决 OCR 噪音插入问题)
        例如: Target="12345", Source="12(date)34(time)5" -> Score close to 100
        """
        # 1. 仅保留字母和数字，忽略符号干扰
        t_clean = "".join(c for c in target if c.isalnum())
        s_clean = "".join(c for c in source if c.isalnum())
        
        if not t_clean or not s_clean:
            return 0.0
            
        # 2. 贪婪匹配子序列
        t_idx, s_idx = 0, 0
        matches = 0
        
        while t_idx < len(t_clean) and s_idx < len(s_clean):
            if t_clean[t_idx] == s_clean[s_idx]:
                matches += 1
                t_idx += 1
                s_idx += 1
            else:
                # 跳过 source 中的噪音字符
                s_idx += 1
        
        # 3. 计算得分
        match_rate = matches / len(t_clean)
        
        # 如果匹配率太低，直接返回
        if match_rate < 0.8:
            return match_rate * 100
            
        # 4. 噪音惩罚 (防止 Target="1", Source="123456789" 这种误判)
        # 计算噪音长度
        noise_len = len(s_clean) - matches
        
        # 允许一定比例的噪音 (例如日期时间插入，通常占总长度的 30%-50%)
        # 如果噪音长度超过目标长度的 60%，开始扣分
        penalty = 0
        if noise_len > len(t_clean) * 0.6:
            excess_noise = noise_len - (len(t_clean) * 0.6)
            penalty = excess_noise * 0.5 # 每多一个噪音字符扣 0.5 分
            penalty = min(penalty, 20)   # 最多扣 20 分
            
        final_score = (match_rate * 100) - penalty
        return max(0, final_score)

    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
        """
        cell_text_normalized = self.text_matcher.normalize_text(cell_text)
        cell_text_processed = self._preprocess_text_for_matching(cell_text)
        
        if len(cell_text_normalized) < 1:
            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:]:
            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]))
                # 🎯 改进：使用空格连接，以便于 token_sort_ratio 进行乱序匹配
                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
            # 🎯 改进：使用预处理后的文本进行 token_sort_ratio 计算
            box_text_processed = self._preprocess_text_for_matching(box['text'])
            
            # token_sort_ratio: 自动分词并排序比较，解决 OCR 结果顺序与 HTML 不一致的问题
            token_sort_sim = fuzz.token_sort_ratio(cell_text_processed, box_text_processed)
            
            # partial_ratio: 子串模糊匹配，解决 OCR 识别错误
            partial_sim = fuzz.partial_ratio(cell_text_normalized, merged_text_normalized)
            
            # 🎯 新增：token_set_ratio (集合匹配)
            # 专门解决：目标文本被 OCR 文本中的噪音隔开的情况
            # 例如 Target="A B", OCR="A noise B" -> token_set_ratio 会很高
            token_set_sim = fuzz.token_set_ratio(cell_text_processed, box_text_processed)

            # 🎯 策略 4: 重构匹配 (Reconstruction Match) - 解决 ID 被噪音打断的问题
            # 逻辑：提取 OCR 中所有属于 Target 子串的 token，拼起来再比
            reconstruct_sim = 0.0
            if len(cell_text_normalized) > 10: # 仅对长文本启用，防止短文本误判
                # 使用预处理后的文本分词 (已处理中文/数字间隔)
                box_tokens = box_text_processed.split()
                # 筛选出所有是目标文本子串的 token
                valid_tokens = []
                for token in box_tokens:
                    # 忽略太短的 token (除非目标也很短)，防止 "1" 这种误匹配
                    if len(token) < 2 and len(cell_text_normalized) > 5:
                        continue
                    if token in cell_text_normalized:
                        valid_tokens.append(token)
                
                if valid_tokens:
                    # 拼接回原始形态
                    reconstructed_text = "".join(valid_tokens)
                    reconstruct_sim = fuzz.ratio(cell_text_normalized, reconstructed_text)
                    if reconstruct_sim > 90:
                         print(f"         🧩 重构匹配生效: '{reconstructed_text}' (sim={reconstruct_sim})")

            # 🎯 策略 5: 子序列匹配 (Subsequence Match) - 解决粘连噪音问题
            # 专门针对: '1544...1050' + '2024-08-10' + '0433...' 这种场景
            subseq_sim = 0.0
            if len(cell_text_normalized) > 8: # 仅对较长文本启用
                subseq_sim = self._calculate_subsequence_score(cell_text_normalized, merged_text_normalized)
                # 🛡️ 关键修复：长度和类型防御
                if subseq_sim > 80:
                    len_cell = len(cell_text_normalized)
                    len_box = len(merged_text_normalized)
                    
                    # 1. 长度差异过大 (Box 比 Cell 长很多)
                    if len_box > len_cell * 1.5:
                        # 2. 且 Cell 是数字/日期/时间类型 (容易在长ID中误配)
                        import re
                        # 匹配纯数字、日期时间格式
                        if re.match(r'^[\d\-\:\.\s]+$', cell_text_normalized):
                            print(f"         ⚠️ 拒绝子序列匹配: 长度差异大且为数字类型 (sim={subseq_sim})")
                            subseq_sim = 0.0

                if subseq_sim > 90:
                    print(f"         🔗 子序列匹配生效: '{cell_text[:10]}...' (sim={subseq_sim:.1f})")

            # 综合得分：取五者最大值
            similarity = max(token_sort_sim, partial_sim, token_set_sim, reconstruct_sim, subseq_sim)

            # 🎯 子串匹配加分
            if is_substring:
                similarity = min(100, similarity + 10)
            
            # 🎯 长度惩罚：如果 box 内容比 cell 多太多（例如吞了下一个单元格），扣分
            # 注意：token_set_ratio 对长度不敏感，所以这里必须严格检查长度，防止误判
            # 只有当 similarity 很高时才检查，防止误杀
            if similarity > 80:
                len_cell = len(cell_text_normalized)
                len_box = len(merged_text_normalized)
                
                # 如果是 token_set_sim 贡献的高分，说明 OCR 里包含了很多噪音
                # 我们需要确保这些噪音不是“下一个单元格的内容”
                # 这里可以加一个更严格的长度检查，或者检查是否包含换行符等
                if len_box > len_cell * 2.0 + 10: # 放宽一点，因为 token_set 本来就是处理噪音的
                     similarity -= 10 # 稍微扣一点分，表示虽然全找到了，但噪音太多不太完美
            
            if similarity >= self.text_matcher.similarity_threshold:
                print(f"         ✓ 匹配成功: '{cell_text[:15]}' vs '{box['text'][:15]}' (相似度: {similarity})")
                # 由于是模糊匹配，返回第一个未使用的 box 作为 last_index
                for b in boxes:
                    if not b.get('used'):
                        last_idx = max(boxes.index(b)-1, 0)
                        break
                return self._build_match_result(box['sorted_combo'], box['text'], similarity, max(start_idx, last_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（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[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,  # ✅ 使用原始坐标
            'text': text,
            'score': score,
            'paddle_indices': [b['paddle_bbox_index'] for b in boxes],
            'used_boxes': boxes,
            'last_used_index': last_index
        }