贪婪算法 (Greedy)匹配行内单元格的终版

merger/table_cell_matcher_greedy.py

@@ -0,0 +1,1173 @@
+"""
+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,
+                 inclination_threshold: float = 0.3):
+        """
+        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
+        self.inclination_threshold = inclination_threshold  # 倾斜校正阈值（度数）
+    
+    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,
+            inclination_threshold=self.inclination_threshold
+        )
+        
+        # 🔑 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,
+                                    inclination_threshold: float = 0.3) -> 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) > inclination_threshold:
+                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[first_unused_idx:] 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)
+            
+            # 🛡️ 增强版防御：防止“短文本”误匹配“长文本”
+            if partial_sim > 80:
+                len_cell = len(cell_text_normalized)
+                len_box = len(merged_text_normalized)
+                
+                # 确定短方和长方
+                if len_cell < len_box:
+                    len_short, len_long = len_cell, len_box
+                    text_short = cell_text_normalized
+                    text_long = merged_text_normalized
+                else:
+                    len_short, len_long = len_box, len_cell
+                    text_short = merged_text_normalized
+                    text_long = cell_text_normalized
+                
+                # 🎯 修正：检测有效内容 (字母、数字、汉字)
+                # 使用 Unicode 范围匹配汉字: \u4e00-\u9fa5
+                import re
+                def has_valid_content(text):
+                    return bool(re.search(r'[a-zA-Z0-9\u4e00-\u9fa5]', text))
+
+                short_has_content = has_valid_content(text_short)
+                long_has_content = has_valid_content(text_long)
+                
+                # 🛑 拒绝条件 1: 短方是纯符号 (无有效内容)，且长方有内容
+                # 例如: Cell="-" vs Box="-200" (拦截)
+                # 例如: Cell="中国银行" vs Box="中国银行储蓄卡" (不拦截，因为都有汉字)
+                if not short_has_content and long_has_content:
+                     # 允许例外：如果长方也很短 (比如 Cell="-" Box="- ")，可能只是多了个空格，不拦截
+                     if len_long > len_short + 2:
+                        print(f"         ⚠️ 拒绝纯符号部分匹配: '{cell_text}' vs '{merged_text_normalized}'")
+                        partial_sim = 0.0
+
+                # 🛑 拒绝条件 2: 短方虽然有内容，但太短了 (信息量不足)
+                elif short_has_content:
+                    # 如果短方只有 1 个字符，且长方超过 3 个字符 -> 拒绝
+                    if len_short == 1 and len_long > 3:
+                        print(f"         ⚠️ 拒绝单字符部分匹配: '{cell_text}' vs '{merged_text_normalized}'")
+                        partial_sim = 0.0
+                    # 如果短方只有 2 个字符，且长方超过 8 个字符 -> 拒绝
+                    elif len_short == 2 and len_long > 8:
+                        print(f"         ⚠️ 拒绝微小碎片部分匹配: '{cell_text}' vs '{merged_text_normalized}'")
+                        partial_sim = 0.0
+
+                    # 🆕 新增条件 3: 覆盖率过低 (防止 "2024" 匹配 "ID2024...")
+                    # 场景: Cell 是长文本, Box 是短文本, 恰好包含在 Cell 中
+                    # 逻辑: 如果覆盖率 < 30% 且 整体相似度(token_sort) < 45，说明 Box 缺失了 Cell 的绝大部分内容
+                    else:
+                        coverage = len_short / len_long if len_long > 0 else 0
+                        if coverage < 0.3 and token_sort_sim < 45:
+                             print(f"         ⚠️ 拒绝低覆盖率部分匹配: '{text_short}' in '{text_long}' (cov={coverage:.2f})")
+                             partial_sim = 0.0
+
+            # 🎯 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
+                        import re
+                        if re.match(r'^[\d\-\:\.\s]+$', cell_text_normalized):
+                            # 🧠 智能豁免：如果 Cell 本身很长 (例如 > 12字符)，说明是长ID
+                            # 长ID即使夹杂了噪音 (如 "ID...日期...文字")，只要子序列匹配高，通常也是对的
+                            # 只有短文本 (如 "2024") 才需要严格防御
+                            if len_cell < 12:
+                                print(f"         ⚠️ 拒绝子序列匹配: 长度差异大且为短数字类型 (sim={subseq_sim})")
+                                subseq_sim = 0.0
+                            else:
+                                print(f"         ✅ 接受长ID子序列匹配: 尽管长度差异大，但特征显著 (len={len_cell})")
+
+                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
+        }