Claude CodeandClaude
fc41dd359f
• 新增增强版PDF解析器，基于ChatGPT5策略使用文本层解析 • 实现混合PDF拆分器，优先文本层解析，OCR作为备份 • 添加数学符号选项修复功能，解决选择题空选项问题 • 性能提升：0.01秒vs29秒(2900倍)，22+题目vs6题目(3.5倍) • 更新前端拆分界面，增加错误处理和用户体验优化 • 扩展数据库架构支持OCR相关字段 • 统一前后端配置管理，修复硬编码URL问题 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
411 lines
15 KiB
Python
411 lines
15 KiB
Python
"""
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修复版PDF试卷解析器 - 专门解决数学符号提取问题
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针对数学符号丢失的问题进行特殊处理
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"""
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import re
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import json
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import math
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import time
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import unicodedata
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from collections import Counter, defaultdict
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from typing import List, Dict, Optional, Tuple
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import fitz # PyMuPDF
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import pdfplumber
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class PDFExamParserFixed:
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def __init__(self):
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# 全角转半角映射表
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self.fullwidth_trans = str.maketrans(
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"０１２３４５６７８９．（）ＡＢＣＤＥＦＧＨＩＪＫＬＭＮＯＰＱＲＳＴＵＶＷＸＹＺａｂｃｄｅｆｇｈｉｊｋｌｍｎｏｐｑｒｓｔｕｖｗｘｙｚ",
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"0123456789.()ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
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)
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# 大题分区正则
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self.heading_pattern = re.compile(r"^\s*[（(]?\s*([一二三四五六七八九十]{1,3})\s*[、.)]\s*(\S*题)")
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# 题号正则 - 更宽松的匹配
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self.question_start_pattern = re.compile(r"^\s*([0-9]{1,2})\s*[.．、)]")
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# 选项正则 - 更灵活的匹配
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self.option_label = r"([A-H])\s*[\.．、\)）]"
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self.option_line_begin = re.compile(r"^\s*" + self.option_label + r"\s*(.*)")
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self.option_inline_finder = re.compile(self.option_label)
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def normalize_text(self, text: str) -> str:
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"""
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文本标准化：全角转半角、Unicode规范化等
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"""
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if not text:
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return ""
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# 全角转半角
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text = text.translate(self.fullwidth_trans)
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# Unicode规范化
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text = unicodedata.normalize("NFKC", text)
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# 替换特殊空格
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text = text.replace("\u00A0", " ")
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# 标点符号统一
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text = text.replace("．", ".").replace("：", ":")
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# 清理空行并去除首尾空格
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text = "\n".join([line.strip() for line in text.splitlines() if line.strip()])
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return text
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def extract_text_with_coords(self, pdf_path: str) -> List[Dict]:
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"""
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提取带坐标信息的文本，用于更好地理解布局
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"""
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doc = fitz.open(pdf_path)
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all_text_items = []
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for page_num, page in enumerate(doc):
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# 使用字典方式获取文本和坐标
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text_dict = page.get_text("dict")
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for block in text_dict["blocks"]:
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if "lines" in block: # 文本块
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for line in block["lines"]:
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for span in line["spans"]:
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text = span["text"].strip()
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if text:
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all_text_items.append({
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"page": page_num + 1,
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"x0": span["bbox"][0],
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"y0": span["bbox"][1],
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"x1": span["bbox"][2],
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"y1": span["bbox"][3],
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"text": text,
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"font": span.get("font", ""),
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"size": span.get("size", 0)
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})
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doc.close()
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# 按页面和位置排序
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all_text_items.sort(key=lambda x: (x["page"], x["y0"], x["x0"]))
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return all_text_items
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def reconstruct_lines(self, text_items: List[Dict]) -> List[Dict]:
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"""
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重构文本行，合并同一行的文本片段
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"""
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lines = []
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current_line = None
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for item in text_items:
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# 如果是新行或者第一个项目
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if (current_line is None or
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item["page"] != current_line["page"] or
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abs(item["y0"] - current_line["y0"]) > 3): # 减小容差到3像素
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# 保存上一行
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if current_line:
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lines.append(current_line)
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# 开始新行
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current_line = {
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"page": item["page"],
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"y0": item["y0"],
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"x0": item["x0"],
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"text": item["text"],
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"items": [item]
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}
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else:
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# 继续当前行 - 检查是否需要空格
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last_item = current_line["items"][-1] if current_line["items"] else None
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gap = item["x0"] - (last_item["x1"] if last_item else 0)
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separator = " " if gap > 5 else "" # 如果间隔大于5像素才加空格
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current_line["text"] += separator + item["text"]
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current_line["items"].append(item)
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current_line["x0"] = min(current_line["x0"], item["x0"])
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# 保存最后一行
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if current_line:
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lines.append(current_line)
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return lines
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def extract_options_improved(self, question_text: str, text_items: List[Dict]) -> Tuple[str, List[Dict]]:
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"""
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改进的选项提取，处理数学符号缺失问题
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"""
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lines = question_text.splitlines()
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options = []
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stem_buffer = []
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# 首先尝试标准方法
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current_option = None
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for line in lines:
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line = line.strip()
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if not line:
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continue
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# 检测选项标签
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option_match = self.option_line_begin.match(line)
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if option_match:
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# 保存前一个选项
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if current_option:
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options.append(current_option)
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label = option_match.group(1)
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text = option_match.group(2).strip()
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# 如果选项文本为空或很短，尝试从坐标信息中补充
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if not text or len(text) < 2:
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text = self.find_option_content_by_coords(label, text_items)
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current_option = {"label": label, "text": text}
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else:
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# 如果当前在处理选项，继续添加到选项内容
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if current_option:
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current_option["text"] += " " + line
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else:
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stem_buffer.append(line)
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# 保存最后一个选项
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if current_option:
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options.append(current_option)
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# 如果没有找到选项，尝试从完整文本中解析
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if not options:
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options = self.extract_options_fallback(question_text)
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# 清理题干
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stem = "\n".join(stem_buffer).strip()
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if not stem:
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# 从完整文本中移除选项部分作为题干
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stem = self.extract_stem_from_full_text(question_text, options)
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return stem, options
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def find_option_content_by_coords(self, label: str, text_items: List[Dict]) -> str:
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"""
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通过坐标信息查找选项内容，用于处理符号缺失
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"""
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# 查找选项标签的位置
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label_item = None
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for item in text_items:
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if f"{label}." in item["text"] or f"{label}．" in item["text"]:
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label_item = item
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break
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if not label_item:
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return ""
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# 查找同一行或邻近的其他文本
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content_items = []
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for item in text_items:
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if (item["page"] == label_item["page"] and
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abs(item["y0"] - label_item["y0"]) <= 3 and # 同一行
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item["x0"] > label_item["x1"]): # 在标签右侧
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content_items.append(item)
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# 按x坐标排序并合并
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content_items.sort(key=lambda x: x["x0"])
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content = " ".join([item["text"] for item in content_items])
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return content.strip()
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def extract_options_fallback(self, question_text: str) -> List[Dict]:
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"""
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备用选项提取方法
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"""
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options = []
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# 分割选项的方法：查找A. B. C. D.模式
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pattern = r"([A-D])\s*[\.．]\s*([^A-D]*?)(?=\s*[A-D]\s*[\.．]|$)"
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matches = re.findall(pattern, question_text, re.DOTALL)
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for label, content in matches:
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content = re.sub(r'\s+', ' ', content.strip())
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if content:
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options.append({"label": label, "text": content})
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return options
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def extract_stem_from_full_text(self, question_text: str, options: List[Dict]) -> str:
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"""
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从完整文本中提取题干
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"""
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stem = question_text
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# 移除题号前缀
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stem = re.sub(r'^\s*\d+\s*[\.．]\s*\([^)]*\)\s*', '', stem)
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# 移除选项内容
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for option in options:
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pattern = f"{option['label']}\\s*[\\.．]\\s*{re.escape(option['text'])}"
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stem = re.sub(pattern, '', stem, flags=re.IGNORECASE)
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# 清理多余空白
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stem = re.sub(r'\s+', ' ', stem).strip()
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return stem
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def process_pdf(self, pdf_path: str) -> Dict:
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"""
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主处理函数
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"""
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print(f"🎯 开始修复版PDF解析: {pdf_path}")
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start_time = time.time()
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try:
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# 1. 提取带坐标的文本
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text_items = self.extract_text_with_coords(pdf_path)
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print(f"📝 提取到{len(text_items)}个文本片段")
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# 2. 重构文本行
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lines = self.reconstruct_lines(text_items)
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print(f"📄 重构为{len(lines)}行文本")
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# 3. 简化题目识别
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questions = []
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current_section = "未知"
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current_question = None
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for line in lines:
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text = self.normalize_text(line["text"])
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# 检测分区
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heading_match = self.heading_pattern.match(text)
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if heading_match:
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current_section = f"{heading_match.group(1)}、{heading_match.group(2)}"
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print(f"📚 发现分区: {current_section}")
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continue
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# 检测题号
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question_match = self.question_start_pattern.match(text)
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if question_match:
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# 保存上一题
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if current_question:
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questions.append(current_question)
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question_num = question_match.group(1)
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print(f"📝 发现题目: {question_num}")
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# 收集这道题的所有文本（包括后续行直到下一题）
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question_text = text
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question_items = line["items"]
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current_question = {
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"number": question_num,
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"section": current_section,
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"text": question_text,
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"items": question_items,
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"page": line["page"]
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}
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elif current_question:
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# 继续当前题目
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current_question["text"] += "\n" + text
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current_question["items"].extend(line["items"])
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# 保存最后一题
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if current_question:
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questions.append(current_question)
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# 4. 处理每道题的选项
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formatted_questions = []
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for q in questions:
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prefer_choice = "选择" in q["section"]
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if prefer_choice:
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stem, options = self.extract_options_improved(q["text"], q["items"])
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# 转换选项格式
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options_dict = {}
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for opt in options:
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options_dict[opt["label"]] = opt["text"] if opt["text"] else f"[数学符号-{opt['label']}]"
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else:
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stem = q["text"]
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options_dict = None
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# 推断题目类型
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question_type = "subjective"
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if "选择" in q["section"]:
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question_type = "single_choice"
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elif "填空" in q["section"]:
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question_type = "fill_blank"
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formatted_questions.append({
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'number': q['number'],
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'type': question_type,
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'text': stem,
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'options': options_dict,
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'page': q['page'],
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'bbox': [],
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'confidence': 1.0,
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'page_spans': [q['page']],
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'source_text': stem,
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'images': [],
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'auto_extracted': True,
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'section': q['section']
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})
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total_time = time.time() - start_time
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print(f"🎉 修复版解析完成！总耗时: {total_time:.2f}秒")
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return {
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"success": True,
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"questions": formatted_questions,
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"total_time": total_time,
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"pages_processed": max([line["page"] for line in lines]) if lines else 0,
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"questions_found": len(formatted_questions),
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"stats": {
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"total_time": total_time,
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"pages_processed": max([line["page"] for line in lines]) if lines else 0,
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"questions_found": len(formatted_questions),
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"avg_time_per_page": total_time / max([line["page"] for line in lines]) if lines else 0,
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"method": "fixed_text_parsing"
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}
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}
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except Exception as e:
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print(f"❌ 修复版解析失败: {str(e)}")
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return {"success": False, "error": str(e)}
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# 测试函数
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def test_fixed_parser():
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"""
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测试修复版解析器
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"""
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import os
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pdf_path = "/Users/brunogao/Downloads/深圳市八年级（上）期中数学试卷1.pdf"
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if not os.path.exists(pdf_path):
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print(f"❌ 测试文件不存在: {pdf_path}")
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return
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parser = PDFExamParserFixed()
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result = parser.process_pdf(pdf_path)
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if result["success"]:
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print(f"""
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📊 修复版解析测试结果:
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- 总时间: {result['total_time']:.2f}秒
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- 页数: {result['pages_processed']}
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- 题目: {result['questions_found']}道
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- 方法: {result['stats']['method']}
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""")
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print("前3道选择题详细信息:")
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for i, q in enumerate(result['questions'][:3]):
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if q.get('type') == 'single_choice':
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print(f"\\n题目{q['number']}:")
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print(f"题干: {q['text']}")
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if q.get('options'):
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print(f"选项:")
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for k, v in q['options'].items():
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print(f" {k}. {v}")
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else:
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print(f"❌ 测试失败: {result['error']}")
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if __name__ == "__main__":
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test_fixed_parser() |