"""
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）
inclination_threshold: 倾斜校正阈值（度数）
"""
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：使用行级动态规划）
"""
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")
# 🔑 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'])
# 🔑 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, 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 DP 进行行内匹配
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'])
# 再次按 x 排序确保顺序
current_boxes.sort(key=lambda x: x['bbox'][0])
html_cells = row.find_all(['td', 'th'])
if not html_cells:
continue
# 🎯 核心变更：使用行级 DP 替代原来的顺序匹配
# 输入：HTML 单元格列表, OCR Box 列表
# 输出：匹配结果列表
dp_results = self._match_cells_in_row_dp(html_cells, current_boxes)
print(f" 行 {row_idx + 1}: {len(html_cells)} 列, 匹配到 {len(dp_results)} 个单元格")
# 解析 DP 结果并填充 cells 列表
for res in dp_results:
cell_idx = res['cell_idx']
match_info = res['match_info']
cell_element = html_cells[cell_idx]
cell_text = cell_element.get_text(strip=True)
matched_boxes = match_info['boxes']
matched_text = match_info['text']
score = match_info['score']
# 标记 box 为已使用
paddle_indices = []
for box in matched_boxes:
box['used'] = True
paddle_indices.append(box.get('paddle_bbox_index', -1))
# 计算合并后的 bbox (使用原始坐标 original_bbox 优先)
merged_bbox = self._merge_boxes_bbox(matched_boxes)
# 注入 HTML 属性
cell_element['data-bbox'] = f"[{merged_bbox[0]},{merged_bbox[1]},{merged_bbox[2]},{merged_bbox[3]}]"
cell_element['data-score'] = f"{score:.4f}"
cell_element['data-paddle-indices'] = str(paddle_indices)
# 构建返回结构 (保持与原函数一致)
cells.append({
'type': 'table_cell',
'text': cell_text,
'matched_text': matched_text,
'bbox': merged_bbox,
'row': row_idx + 1,
'col': cell_idx + 1,
'score': score,
'paddle_bbox_indices': paddle_indices
})
print(f" 列 {cell_idx + 1}: '{cell_text[:15]}...' 匹配 {len(matched_boxes)} 个box (分值: {score:.1f})")
# 计算新的指针位置 (逻辑保持不变：基于 used 标记)
used_count = sum(1 for box in table_region_boxes if box.get('used'))
new_pointer = start_pointer + used_count
print(f" 总计匹配: {len(cells)} 个单元格")
return str(soup), cells, new_pointer
def _merge_boxes_bbox(self, boxes: List[Dict]) -> List[int]:
"""辅助函数：合并多个 box 的坐标"""
if not boxes:
return [0, 0, 0, 0]
# 优先使用 original_bbox，如果没有则使用 bbox
def get_coords(b):
return b.get('original_bbox', b['bbox'])
x1 = min(get_coords(b)[0] for b in boxes)
y1 = min(get_coords(b)[1] for b in boxes)
x2 = max(get_coords(b)[2] for b in boxes)
y2 = max(get_coords(b)[3] for b in boxes)
return [x1, y1, x2, y2]
def _match_cells_in_row_dp(self, html_cells: List, row_boxes: List[Dict]) -> List[Dict]:
"""
使用动态规划进行行内单元格匹配
目标：找到一种分配方案，使得整行的匹配总分最高
"""
n_cells = len(html_cells)
n_boxes = len(row_boxes)
# dp[i][j] 表示：前 i 个单元格 消耗了 前 j 个 boxes 的最大得分
dp = np.full((n_cells + 1, n_boxes + 1), -np.inf)
dp[0][0] = 0
# path[i][j] = (prev_j, matched_info) 用于回溯
path = {}
# 允许合并的最大 box 数量
MAX_MERGE = 5
for i in range(1, n_cells + 1):
cell = html_cells[i-1]
cell_text = cell.get_text(strip=True)
# 如果单元格为空，允许继承状态（相当于跳过该单元格）
if not cell_text:
for j in range(n_boxes + 1):
if dp[i-1][j] > -np.inf:
dp[i][j] = dp[i-1][j]
path[(i, j)] = (j, None)
continue
# 遍历当前 box 指针 j
for j in range(n_boxes + 1):
# 策略 A: 当前单元格不匹配任何 box (Cell Missing / OCR漏检)
if dp[i-1][j] > dp[i][j]:
dp[i][j] = dp[i-1][j]
path[(i, j)] = (j, None)
# 策略 B: 当前单元格匹配了 k 个 boxes (从 prev_j 到 j)
# 限制搜索范围：最多往前看 MAX_MERGE 个 box
search_limit = max(0, j - MAX_MERGE)
# 允许中间跳过少量噪音 box (例如 prev_j 到 j 之间跨度大，但只取了部分)
# 但为了简化，这里假设是连续取用 row_boxes[prev_j:j]
for prev_j in range(j - 1, search_limit - 1, -1):
if dp[i-1][prev_j] == -np.inf:
continue
candidate_boxes = row_boxes[prev_j:j]
# 组合文本 (使用空格连接)
merged_text = " ".join([b['text'] for b in candidate_boxes])
# 计算得分
score = self._compute_match_score(cell_text, merged_text)
# 只有及格的匹配才考虑
if score > 50:
new_score = dp[i-1][prev_j] + score
if new_score > dp[i][j]:
dp[i][j] = new_score
path[(i, j)] = (prev_j, {
'text': merged_text,
'boxes': candidate_boxes,
'score': score
})
# --- 回溯找最优解 ---
best_j = np.argmax(dp[n_cells])
if dp[n_cells][best_j] == -np.inf:
return []
results = []
curr_i, curr_j = n_cells, best_j
while curr_i > 0:
step_info = path.get((curr_i, curr_j))
if step_info:
prev_j, match_info = step_info
if match_info:
results.append({
'cell_idx': curr_i - 1,
'match_info': match_info
})
curr_j = prev_j
curr_i -= 1
return results[::-1]
def _compute_match_score(self, cell_text: str, box_text: str) -> float:
"""
纯粹的评分函数：计算单元格文本与候选 Box 文本的匹配得分
包含所有防御逻辑
"""
# 1. 预处理
cell_norm = self.text_matcher.normalize_text(cell_text)
box_norm = self.text_matcher.normalize_text(box_text)
if not cell_norm or not box_norm:
return 0.0
# --- ⚡️ 快速防御 ---
len_cell = len(cell_norm)
len_box = len(box_norm)
# 长度差异过大直接 0 分 (除非是包含关系且特征明显)
if len_box > len_cell * 3 + 5:
if len_cell < 5: return 0.0
# --- 🔍 核心相似度计算 ---
cell_proc = self._preprocess_text_for_matching(cell_text)
box_proc = self._preprocess_text_for_matching(box_text)
# A. Token Sort (解决乱序)
score_sort = fuzz.token_sort_ratio(cell_proc, box_proc)
# B. Partial (解决截断/包含)
score_partial = fuzz.partial_ratio(cell_norm, box_norm)
# C. Subsequence (解决噪音插入)
score_subseq = 0.0
if len_cell > 5:
score_subseq = self._calculate_subsequence_score(cell_norm, box_norm)
# --- 🛡️ 深度防御逻辑 ---
# 1. 短文本防御
if score_partial > 80:
import re
has_content = lambda t: bool(re.search(r'[a-zA-Z0-9\u4e00-\u9fa5]', t))
# 纯符号防御
if not has_content(cell_norm) and has_content(box_norm):
if len_box > len_cell + 2: score_partial = 0.0
# 微小碎片防御
elif len_cell <= 2 and len_box > 8:
score_partial = 0.0
# 覆盖率防御
else:
coverage = len_cell / len_box if len_box > 0 else 0
if coverage < 0.3 and score_sort < 45:
score_partial = 0.0
# 2. 子序列防御
if score_subseq > 80:
if len_box > len_cell * 1.5:
import re
if re.match(r'^[\d\-\:\.\s]+$', cell_norm) and len_cell < 12:
score_subseq = 0.0
# --- 📊 综合评分 ---
final_score = max(score_sort, score_partial, score_subseq)
# 精确匹配奖励
if cell_norm == box_norm:
final_score = 100.0
elif cell_norm in box_norm:
final_score = min(100, final_score + 5)
return final_score
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)