feat: 实现高效PDF试卷题目提取系统
• 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 <noreply@anthropic.com>
This commit is contained in:
Claude Code
Claude
+172
-1
@@ -1,7 +1,8 @@
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from fastapi import FastAPI, HTTPException, Depends, UploadFile, File, status, Request
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from fastapi.responses import FileResponse
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
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from sqlalchemy import create_engine, Column, Integer, String, Float, DateTime, Boolean, Text, JSON
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from sqlalchemy import create_engine, Column, Integer, String, Float, DateTime, Boolean, Text, JSON, func
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from sqlalchemy.ext.declarative import declarative_base
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from sqlalchemy.orm import sessionmaker, Session
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from pydantic import BaseModel
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@@ -13,6 +14,10 @@ from datetime import datetime, timedelta, date
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import json
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import io
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import csv
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import shutil
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import tempfile
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import uuid
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from pdf_splitter_hybrid import PDFSplitterHybrid as PDFSplitter
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import uvicorn
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import uuid
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import os
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@@ -143,6 +148,14 @@ class Question(Base):
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explanation = Column(Text, nullable=True) # 解析
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image_key = Column(String, nullable=True) # 题目图片
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tags = Column(JSON, nullable=True) # 标签数组
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# 拆题功能相关字段
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page_spans = Column(JSON, nullable=True) # 页面位置信息 [{"page":1,"bbox":[x,y,w,h]}]
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source_text = Column(Text, nullable=True) # OCR原始文本
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images = Column(JSON, nullable=True) # 题目中的图片信息 [{"page":1,"bbox":[...],"key":"..."}]
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formula = Column(JSON, nullable=True) # 公式信息 [{"latex":"...","bbox":[...]}]
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score = Column(Integer, nullable=True) # 分值
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question_number = Column(String, nullable=True) # 题号 (1, 2, 3 等)
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auto_extracted = Column(Boolean, default=False) # 是否自动提取
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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@@ -343,6 +356,28 @@ class QuestionCollectionResponse(BaseModel):
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class Config:
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from_attributes = True
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# 拆题相关模型
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class SplitRequest(BaseModel):
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paper_id: int
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class SplitQuestionResult(BaseModel):
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number: str
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type: str
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text: str
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options: Optional[dict] = None
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bbox: List[int]
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page: int
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confidence: float
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image_filename: Optional[str] = None
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class SplitResponse(BaseModel):
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success: bool
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paper_id: int
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total_questions: int
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questions: List[SplitQuestionResult]
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stats: dict
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error: Optional[str] = None
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# FastAPI应用
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app = FastAPI(title="WordMaster Pro API", version="1.0.0")
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@@ -1477,5 +1512,141 @@ async def serve_uploaded_file(
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else:
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raise HTTPException(status_code=404, detail="File not found")
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# PDF拆题API
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@app.post("/api/papers/{paper_id}/split", response_model=SplitResponse)
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async def split_paper(
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paper_id: int,
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current_user: User = Depends(get_current_user),
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db: Session = Depends(get_db)
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):
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"""
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拆解试卷PDF，自动提取题目
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"""
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# 检查试卷是否存在
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paper = db.query(Paper).filter(
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Paper.id == paper_id,
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Paper.user_id == current_user.id
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).first()
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if not paper:
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raise HTTPException(status_code=404, detail="试卷不存在")
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# 检查文件是否存在
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file_path = paper.file_key
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if not file_path or not os.path.exists(file_path):
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raise HTTPException(status_code=404, detail="试卷文件不存在")
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try:
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# 创建PDF拆分器
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splitter = PDFSplitter()
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# 创建临时目录存储处理结果
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temp_dir = tempfile.mkdtemp()
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try:
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# 处理PDF
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result = splitter.process_pdf(file_path, temp_dir)
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if not result['success']:
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return SplitResponse(
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success=False,
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paper_id=paper_id,
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total_questions=0,
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questions=[],
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stats={},
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error=result.get('error', '未知错误')
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)
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questions = result['questions']
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stats = result['stats']
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# 保存提取的题目到数据库
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saved_questions = []
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for i, question_data in enumerate(questions):
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# 移动题目图片到uploads目录
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image_key = None
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if question_data.get('image_path') and os.path.exists(question_data['image_path']):
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# 生成新的文件名
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file_extension = os.path.splitext(question_data['image_filename'])[1]
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new_filename = f"{uuid.uuid4()}{file_extension}"
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dest_dir = os.path.join("uploads", str(current_user.id))
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os.makedirs(dest_dir, exist_ok=True)
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dest_path = os.path.join(dest_dir, new_filename)
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try:
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shutil.copy2(question_data['image_path'], dest_path)
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image_key = dest_path
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except Exception as e:
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print(f"移动题目图片失败: {e}")
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# 创建题目记录
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db_question = Question(
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user_id=current_user.id,
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paper_id=paper_id,
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question_type=question_data.get('type', 'unknown'),
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subject=paper.subject,
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grade=paper.grade,
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knowledge_points=[], # 后续可以通过AI分析填充
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difficulty=3, # 默认难度
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question_content=question_data.get('text', ''),
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options=question_data.get('options'),
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correct_answer='', # 需要手动填写或AI识别
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explanation=None,
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image_key=image_key,
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# 拆题相关字段
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page_spans=question_data.get('page_spans', [{'page': question_data.get('page', 1), 'bbox': question_data.get('bbox', [])}]),
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source_text=question_data.get('text', ''),
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images=[{
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'page': question_data.get('page', 1),
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'bbox': question_data.get('bbox', []),
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'key': image_key
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}] if image_key else [],
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formula=[], # 后续可以添加公式识别
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score=None, # 需要手动填写
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question_number=question_data.get('number', str(i+1)),
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auto_extracted=True
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)
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db.add(db_question)
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# 构建返回数据
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saved_questions.append(SplitQuestionResult(
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number=question_data.get('number', str(i+1)),
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type=question_data.get('type', 'unknown'),
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text=question_data.get('text', ''),
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options=question_data.get('options'),
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bbox=question_data.get('bbox', []),
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page=question_data.get('page', 1),
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confidence=question_data.get('confidence', 0.0),
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image_filename=new_filename if image_key else None
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))
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# 提交数据库更改
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db.commit()
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return SplitResponse(
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success=True,
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paper_id=paper_id,
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total_questions=len(saved_questions),
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questions=saved_questions,
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stats=stats
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)
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finally:
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# 清理临时目录
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if os.path.exists(temp_dir):
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shutil.rmtree(temp_dir)
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except Exception as e:
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db.rollback()
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return SplitResponse(
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success=False,
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paper_id=paper_id,
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total_questions=0,
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questions=[],
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stats={},
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error=f"处理失败: {str(e)}"
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)
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if __name__ == "__main__":
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uvicorn.run(app, host="0.0.0.0", port=8888)
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@@ -0,0 +1,68 @@
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#!/usr/bin/env python3
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"""
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数据库迁移脚本 - 添加PDF拆题相关字段
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"""
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import sqlite3
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import os
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def migrate_database():
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"""迁移数据库，添加新字段"""
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db_path = "wordmaster.db"
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if not os.path.exists(db_path):
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print("❌ 数据库文件不存在")
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return False
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try:
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conn = sqlite3.connect(db_path)
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cursor = conn.cursor()
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print("🔄 开始数据库迁移...")
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# 检查是否已经存在新字段
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cursor.execute("PRAGMA table_info(questions)")
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columns = [column[1] for column in cursor.fetchall()]
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new_fields = [
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('page_spans', 'JSON'),
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('source_text', 'TEXT'),
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('images', 'JSON'),
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('formula', 'JSON'),
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('score', 'REAL'),
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('question_number', 'VARCHAR'),
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('auto_extracted', 'BOOLEAN DEFAULT FALSE')
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]
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# 添加缺失的字段
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for field_name, field_type in new_fields:
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if field_name not in columns:
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sql = f"ALTER TABLE questions ADD COLUMN {field_name} {field_type}"
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print(f" 添加字段: {field_name}")
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cursor.execute(sql)
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else:
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print(f" 字段已存在: {field_name}")
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conn.commit()
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print("✅ 数据库迁移完成")
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# 验证新结构
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cursor.execute("PRAGMA table_info(questions)")
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new_columns = [column[1] for column in cursor.fetchall()]
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print(f"📊 当前字段数量: {len(new_columns)}")
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return True
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except Exception as e:
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print(f"❌ 迁移失败: {e}")
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return False
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finally:
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if 'conn' in locals():
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conn.close()
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if __name__ == "__main__":
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success = migrate_database()
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if success:
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print("\n🎉 数据库迁移成功完成!")
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else:
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print("\n💔 数据库迁移失败!")
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@@ -0,0 +1,510 @@
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"""
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增强版PDF试卷解析器 - 基于ChatGPT5策略
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核心改进：
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1. 页眉页脚自动过滤
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2. 智能选项识别（行内、行首、续写）
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3. 文本标准化处理
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4. 跨页题目处理
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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 PDFExamParserEnhanced:
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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
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self.question_start_pattern = re.compile(r"^\s*(?:第\s*)?([0-9]{1,3}|[一二三四五六七八九十]{1,3})\s*[.)]")
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# 选项正则
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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_lines_with_pymupdf(self, pdf_path: str) -> List[Dict]:
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"""
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使用PyMuPDF提取文本行，包含页眉页脚过滤
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"""
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doc = fitz.open(pdf_path)
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pages_data = []
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header_candidates = []
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footer_candidates = []
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# 第一遍：收集所有文本块和页眉页脚候选
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for page_num, page in enumerate(doc):
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blocks = page.get_text("blocks")
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page_height = page.rect.height
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page_width = page.rect.width
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local_blocks = []
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for block in blocks:
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x0, y0, x1, y1, text = block[0], block[1], block[2], block[3], block[4]
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text = self.normalize_text(text)
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if not text:
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continue
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block_info = {
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"page": page_num + 1,
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"x0": x0, "y0": y0, "x1": x1, "y1": y1,
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"page_width": page_width, "page_height": page_height,
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"text": text
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}
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local_blocks.append(block_info)
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# 收集页眉页脚候选（顶部12%和底部10%）
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if y0 <= page_height * 0.12 and len(text) <= 80:
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header_candidates.append(text.strip())
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if y1 >= page_height * 0.90 and len(text) <= 80:
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footer_candidates.append(text.strip())
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# 按阅读顺序排序（从上到下，从左到右）
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local_blocks.sort(key=lambda b: (round(b["y0"], 1), round(b["x0"], 1)))
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pages_data.append(local_blocks)
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doc.close()
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# 识别重复的页眉页脚
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n_pages = len(pages_data)
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min_occurrences = max(2, math.ceil(n_pages * 0.6))
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header_common = {
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text for text, count in Counter(header_candidates).items()
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if count >= min_occurrences
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}
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footer_common = {
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text for text, count in Counter(footer_candidates).items()
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if count >= min_occurrences
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}
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print(f"🔍 识别页眉: {header_common}")
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print(f"🔍 识别页脚: {footer_common}")
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# 第二遍：提取文本行，过滤页眉页脚
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lines = []
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for page_blocks in pages_data:
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for block in page_blocks:
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text = block["text"]
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# 过滤页眉页脚
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is_header = (block["y0"] <= block["page_height"] * 0.12 and
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text.strip() in header_common)
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is_footer = (block["y1"] >= block["page_height"] * 0.90 and
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text.strip() in footer_common)
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if is_header or is_footer:
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continue
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# 将文本块拆分为行
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for line in text.splitlines():
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line = line.strip()
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if line:
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lines.append({
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"page": block["page"],
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"y": block["y0"],
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"x": block["x0"],
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"text": line
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})
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# 按页面和位置排序
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lines.sort(key=lambda l: (l["page"], round(l["y"], 1), round(l["x"], 1)))
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print(f"✅ PyMuPDF提取完成: {len(lines)}行")
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return lines
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||||
|
||||
def extract_lines_with_pdfplumber(self, pdf_path: str) -> List[Dict]:
|
||||
"""
|
||||
使用pdfplumber提取文本行（备用方案）
|
||||
"""
|
||||
lines = []
|
||||
|
||||
with pdfplumber.open(pdf_path) as pdf:
|
||||
n_pages = len(pdf.pages)
|
||||
top_texts = []
|
||||
bottom_texts = []
|
||||
|
||||
# 收集页眉页脚候选
|
||||
for i, page in enumerate(pdf.pages, start=1):
|
||||
words = page.extract_words(x_tolerance=2, y_tolerance=2, keep_blank_chars=False)
|
||||
if not words:
|
||||
continue
|
||||
|
||||
# 按位置排序
|
||||
words.sort(key=lambda w: (round(w["top"], 1), round(w["x0"], 1)))
|
||||
|
||||
# 按行分组
|
||||
line_map = defaultdict(list)
|
||||
for word in words:
|
||||
line_map[round(word["top"], 1)].append(word)
|
||||
|
||||
sorted_tops = sorted(line_map.keys())
|
||||
if sorted_tops:
|
||||
# 第一行和最后一行
|
||||
first_line = " ".join([
|
||||
w["text"] for w in sorted(line_map[sorted_tops[0]], key=lambda x: x["x0"])
|
||||
]).strip()
|
||||
last_line = " ".join([
|
||||
w["text"] for w in sorted(line_map[sorted_tops[-1]], key=lambda x: x["x0"])
|
||||
]).strip()
|
||||
|
||||
if first_line:
|
||||
top_texts.append(self.normalize_text(first_line))
|
||||
if last_line:
|
||||
bottom_texts.append(self.normalize_text(last_line))
|
||||
|
||||
# 识别重复的页眉页脚
|
||||
min_occurrences = max(2, math.ceil(n_pages * 0.6))
|
||||
header_common = {
|
||||
text for text, count in Counter(top_texts).items()
|
||||
if count >= min_occurrences
|
||||
}
|
||||
footer_common = {
|
||||
text for text, count in Counter(bottom_texts).items()
|
||||
if count >= min_occurrences
|
||||
}
|
||||
|
||||
# 提取所有行，过滤页眉页脚
|
||||
for i, page in enumerate(pdf.pages, start=1):
|
||||
words = page.extract_words(x_tolerance=2, y_tolerance=2, keep_blank_chars=False)
|
||||
if not words:
|
||||
continue
|
||||
|
||||
# 按行分组
|
||||
line_map = defaultdict(list)
|
||||
for word in words:
|
||||
line_map[round(word["top"], 1)].append(word)
|
||||
|
||||
for top in sorted(line_map.keys()):
|
||||
word_list = sorted(line_map[top], key=lambda x: x["x0"])
|
||||
text = self.normalize_text(" ".join(w["text"] for w in word_list))
|
||||
|
||||
if not text:
|
||||
continue
|
||||
|
||||
# 过滤页眉页脚
|
||||
if text in header_common or text in footer_common:
|
||||
continue
|
||||
|
||||
lines.append({
|
||||
"page": i,
|
||||
"y": top,
|
||||
"x": word_list[0]["x0"],
|
||||
"text": text
|
||||
})
|
||||
|
||||
lines.sort(key=lambda l: (l["page"], round(l["y"], 1), round(l["x"], 1)))
|
||||
|
||||
print(f"✅ pdfplumber提取完成: {len(lines)}行")
|
||||
return lines
|
||||
|
||||
def extract_options_and_stem(self, question_text: str, prefer_choice: bool) -> Tuple[str, List[Dict]]:
|
||||
"""
|
||||
提取题干和选项，支持多种选项格式
|
||||
"""
|
||||
lines = question_text.splitlines()
|
||||
options = []
|
||||
current_option = None
|
||||
stem_buffer = []
|
||||
|
||||
def save_current_option():
|
||||
nonlocal current_option, options
|
||||
if current_option is not None:
|
||||
label, text = current_option
|
||||
options.append({"label": label, "text": text.strip()})
|
||||
current_option = None
|
||||
|
||||
for line in lines:
|
||||
# 检测行内多选项格式：A. xxx；B. yyy；C. zzz
|
||||
inline_matches = list(self.option_inline_finder.finditer(line))
|
||||
if len(inline_matches) >= 2:
|
||||
save_current_option()
|
||||
for idx, match in enumerate(inline_matches):
|
||||
label = match.group(1)
|
||||
start_pos = match.end()
|
||||
end_pos = inline_matches[idx + 1].start() if idx + 1 < len(inline_matches) else len(line)
|
||||
option_text = line[start_pos:end_pos].strip(" ；;，, ")
|
||||
if option_text:
|
||||
options.append({"label": label, "text": option_text})
|
||||
continue
|
||||
|
||||
# 检测行首选项格式：A. xxx
|
||||
line_begin_match = self.option_line_begin.match(line)
|
||||
if line_begin_match:
|
||||
save_current_option()
|
||||
label = line_begin_match.group(1)
|
||||
text = line_begin_match.group(2).strip()
|
||||
current_option = (label, text)
|
||||
continue
|
||||
|
||||
# 选项续行
|
||||
if current_option is not None:
|
||||
current_option = (current_option[0], (current_option[1] + " " + line).strip())
|
||||
else:
|
||||
stem_buffer.append(line)
|
||||
|
||||
save_current_option()
|
||||
|
||||
stem = "\n".join(stem_buffer).strip()
|
||||
|
||||
# 如果不是选择题，保持原文
|
||||
if not prefer_choice:
|
||||
return (question_text.strip(), [])
|
||||
|
||||
# 选择题但没找到选项的情况，尝试贪婪提取
|
||||
if prefer_choice and not options:
|
||||
def extract_option_text(label: str, text: str) -> Optional[str]:
|
||||
pattern = label + r"\s*[\.．、\)）]\s*(.*?)(?=(?:[；;]\s*)?[A-H]\s*[\.．、\)）]|$)"
|
||||
match = re.search(pattern, text, re.S)
|
||||
return match.group(1).strip() if match else None
|
||||
|
||||
for label in "ABCDEFGH":
|
||||
option_text = extract_option_text(label, question_text)
|
||||
if option_text:
|
||||
options.append({"label": label, "text": re.sub(r"\s+", " ", option_text)})
|
||||
|
||||
# 只保留A-D选项
|
||||
options = [opt for opt in options if opt["label"] in "ABCD" and opt["text"]]
|
||||
|
||||
if options:
|
||||
# 从原文中清除选项部分，提取纯题干
|
||||
clean_pattern = r"[A-D]\s*[\.．、\)）]\s*.*?(?=(?:[；;]\s*)?[A-D]\s*[\.．、\)）]|$)"
|
||||
stem_clean = re.sub(clean_pattern, "", question_text, flags=re.S).strip()
|
||||
stem = re.sub(r"[；;]+", " ", stem_clean).strip()
|
||||
|
||||
return (stem if stem else question_text.strip(), options)
|
||||
|
||||
def split_questions(self, lines: List[Dict]) -> List[Dict]:
|
||||
"""
|
||||
拆分题目
|
||||
"""
|
||||
questions = []
|
||||
current_section = None
|
||||
buffer = []
|
||||
start_page = None
|
||||
|
||||
def flush_current_question():
|
||||
nonlocal buffer, start_page, current_section, questions
|
||||
if not buffer:
|
||||
return
|
||||
|
||||
question_text = "\n".join([item["text"] for item in buffer]).strip()
|
||||
first_line = buffer[0]["text"]
|
||||
|
||||
# 提取题号
|
||||
match = self.question_start_pattern.search(first_line)
|
||||
question_number = match.group(1) if match else None
|
||||
|
||||
# 判断是否为选择题
|
||||
prefer_choice = bool(current_section and "选择" in current_section)
|
||||
|
||||
# 提取题干和选项
|
||||
stem, options = self.extract_options_and_stem(question_text, prefer_choice)
|
||||
|
||||
# 推断题目类型
|
||||
question_type = "subjective"
|
||||
if current_section and "选择" in current_section:
|
||||
question_type = "single_choice"
|
||||
elif current_section and "填空" in current_section:
|
||||
question_type = "fill_blank"
|
||||
|
||||
end_page = buffer[-1]["page"]
|
||||
|
||||
question_item = {
|
||||
"section": current_section,
|
||||
"qno": question_number,
|
||||
"type": question_type,
|
||||
"stem": stem,
|
||||
"options": options,
|
||||
"page_start": start_page,
|
||||
"page_end": end_page
|
||||
}
|
||||
|
||||
questions.append(question_item)
|
||||
|
||||
# 重置缓冲区
|
||||
buffer = []
|
||||
start_page = None
|
||||
|
||||
# 逐行处理
|
||||
for line in lines:
|
||||
text = line["text"]
|
||||
|
||||
# 检测大题分区
|
||||
heading_match = self.heading_pattern.match(text)
|
||||
if heading_match:
|
||||
flush_current_question()
|
||||
current_section = f"{heading_match.group(1)}、{heading_match.group(2)}"
|
||||
print(f"📚 发现分区: {current_section}")
|
||||
continue
|
||||
|
||||
# 检测题号
|
||||
question_match = self.question_start_pattern.match(text)
|
||||
if question_match:
|
||||
flush_current_question()
|
||||
buffer = [line]
|
||||
start_page = line["page"]
|
||||
print(f"📝 发现题目: {question_match.group(1)}")
|
||||
else:
|
||||
if buffer:
|
||||
buffer.append(line)
|
||||
# 跳过序言等内容
|
||||
|
||||
# 处理最后一题
|
||||
flush_current_question()
|
||||
|
||||
# 清理无效题目
|
||||
valid_questions = []
|
||||
for question in questions:
|
||||
# 去除题号前缀，检查是否有实际内容
|
||||
content = re.sub(r"^\s*(\d+|[一二三四五六七八九十]+)\s*[.)]\s*", "", question["stem"] or "").strip()
|
||||
if content:
|
||||
valid_questions.append(question)
|
||||
|
||||
return valid_questions
|
||||
|
||||
def process_pdf(self, pdf_path: str) -> Dict:
|
||||
"""
|
||||
主处理函数
|
||||
"""
|
||||
print(f"🎯 开始增强PDF解析: {pdf_path}")
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# 优先使用PyMuPDF
|
||||
lines = self.extract_lines_with_pymupdf(pdf_path)
|
||||
|
||||
if not lines:
|
||||
print("⚠️ PyMuPDF失败，尝试pdfplumber")
|
||||
lines = self.extract_lines_with_pdfplumber(pdf_path)
|
||||
|
||||
if not lines:
|
||||
return {"success": False, "error": "无法提取PDF文本"}
|
||||
|
||||
# 拆分题目
|
||||
questions = self.split_questions(lines)
|
||||
|
||||
# 转换为API格式
|
||||
formatted_questions = []
|
||||
for q in questions:
|
||||
# 转换选项格式
|
||||
options_dict = {}
|
||||
if q.get("options"):
|
||||
for opt in q["options"]:
|
||||
options_dict[opt["label"]] = opt["text"]
|
||||
|
||||
formatted_questions.append({
|
||||
'number': q['qno'],
|
||||
'type': q['type'],
|
||||
'text': q['stem'],
|
||||
'options': options_dict if options_dict else None,
|
||||
'page': q['page_start'],
|
||||
'bbox': [],
|
||||
'confidence': 1.0,
|
||||
'page_spans': list(range(q['page_start'], q['page_end'] + 1)),
|
||||
'source_text': q['stem'],
|
||||
'images': [],
|
||||
'auto_extracted': True,
|
||||
'section': q['section']
|
||||
})
|
||||
|
||||
total_time = time.time() - start_time
|
||||
print(f"🎉 增强解析完成！总耗时: {total_time:.2f}秒")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"questions": formatted_questions,
|
||||
"total_time": total_time,
|
||||
"pages_processed": len(set(line["page"] for line in lines)),
|
||||
"questions_found": len(formatted_questions),
|
||||
"stats": {
|
||||
"total_time": total_time,
|
||||
"pages_processed": len(set(line["page"] for line in lines)),
|
||||
"questions_found": len(formatted_questions),
|
||||
"avg_time_per_page": total_time / len(set(line["page"] for line in lines)) if lines else 0,
|
||||
"method": "enhanced_text_parsing"
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 增强解析失败: {str(e)}")
|
||||
return {"success": False, "error": str(e)}
|
||||
|
||||
# 测试函数
|
||||
def test_enhanced_parser():
|
||||
"""
|
||||
测试增强解析器
|
||||
"""
|
||||
import os
|
||||
import time
|
||||
|
||||
pdf_path = "/Users/brunogao/Downloads/深圳市八年级（上）期中数学试卷1.pdf"
|
||||
|
||||
if not os.path.exists(pdf_path):
|
||||
print(f"❌ 测试文件不存在: {pdf_path}")
|
||||
return
|
||||
|
||||
parser = PDFExamParserEnhanced()
|
||||
result = parser.process_pdf(pdf_path)
|
||||
|
||||
if result["success"]:
|
||||
print(f"""
|
||||
📊 增强解析测试结果:
|
||||
- 总时间: {result['total_time']:.2f}秒
|
||||
- 页数: {result['pages_processed']}
|
||||
- 题目: {result['questions_found']}道
|
||||
- 方法: {result['stats']['method']}
|
||||
""")
|
||||
|
||||
print("前8道题预览:")
|
||||
for i, q in enumerate(result['questions'][:8]):
|
||||
options_str = ""
|
||||
if q.get('options'):
|
||||
options_str = f" 选项: {', '.join([f'{k}. {v[:20]}...' for k, v in q['options'].items()])}"
|
||||
|
||||
print(f"题目{q['number']} ({q.get('type', 'unknown')}): {q['text'][:60]}...{options_str}")
|
||||
else:
|
||||
print(f"❌ 测试失败: {result['error']}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
import time
|
||||
test_enhanced_parser()
|
||||
@@ -0,0 +1,411 @@
|
||||
"""
|
||||
修复版PDF试卷解析器 - 专门解决数学符号提取问题
|
||||
针对数学符号丢失的问题进行特殊处理
|
||||
"""
|
||||
|
||||
import re
|
||||
import json
|
||||
import math
|
||||
import time
|
||||
import unicodedata
|
||||
from collections import Counter, defaultdict
|
||||
from typing import List, Dict, Optional, Tuple
|
||||
import fitz # PyMuPDF
|
||||
import pdfplumber
|
||||
|
||||
class PDFExamParserFixed:
|
||||
def __init__(self):
|
||||
# 全角转半角映射表
|
||||
self.fullwidth_trans = str.maketrans(
|
||||
"０１２３４５６７８９．（）ＡＢＣＤＥＦＧＨＩＪＫＬＭＮＯＰＱＲＳＴＵＶＷＸＹＺａｂｃｄｅｆｇｈｉｊｋｌｍｎｏｐｑｒｓｔｕｖｗｘｙｚ",
|
||||
"0123456789.()ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
|
||||
)
|
||||
|
||||
# 大题分区正则
|
||||
self.heading_pattern = re.compile(r"^\s*[（(]?\s*([一二三四五六七八九十]{1,3})\s*[、.)]\s*(\S*题)")
|
||||
|
||||
# 题号正则 - 更宽松的匹配
|
||||
self.question_start_pattern = re.compile(r"^\s*([0-9]{1,2})\s*[.．、)]")
|
||||
|
||||
# 选项正则 - 更灵活的匹配
|
||||
self.option_label = r"([A-H])\s*[\.．、\)）]"
|
||||
self.option_line_begin = re.compile(r"^\s*" + self.option_label + r"\s*(.*)")
|
||||
self.option_inline_finder = re.compile(self.option_label)
|
||||
|
||||
def normalize_text(self, text: str) -> str:
|
||||
"""
|
||||
文本标准化：全角转半角、Unicode规范化等
|
||||
"""
|
||||
if not text:
|
||||
return ""
|
||||
|
||||
# 全角转半角
|
||||
text = text.translate(self.fullwidth_trans)
|
||||
|
||||
# Unicode规范化
|
||||
text = unicodedata.normalize("NFKC", text)
|
||||
|
||||
# 替换特殊空格
|
||||
text = text.replace("\u00A0", " ")
|
||||
|
||||
# 标点符号统一
|
||||
text = text.replace("．", ".").replace("：", ":")
|
||||
|
||||
# 清理空行并去除首尾空格
|
||||
text = "\n".join([line.strip() for line in text.splitlines() if line.strip()])
|
||||
|
||||
return text
|
||||
|
||||
def extract_text_with_coords(self, pdf_path: str) -> List[Dict]:
|
||||
"""
|
||||
提取带坐标信息的文本，用于更好地理解布局
|
||||
"""
|
||||
doc = fitz.open(pdf_path)
|
||||
all_text_items = []
|
||||
|
||||
for page_num, page in enumerate(doc):
|
||||
# 使用字典方式获取文本和坐标
|
||||
text_dict = page.get_text("dict")
|
||||
|
||||
for block in text_dict["blocks"]:
|
||||
if "lines" in block: # 文本块
|
||||
for line in block["lines"]:
|
||||
for span in line["spans"]:
|
||||
text = span["text"].strip()
|
||||
if text:
|
||||
all_text_items.append({
|
||||
"page": page_num + 1,
|
||||
"x0": span["bbox"][0],
|
||||
"y0": span["bbox"][1],
|
||||
"x1": span["bbox"][2],
|
||||
"y1": span["bbox"][3],
|
||||
"text": text,
|
||||
"font": span.get("font", ""),
|
||||
"size": span.get("size", 0)
|
||||
})
|
||||
|
||||
doc.close()
|
||||
|
||||
# 按页面和位置排序
|
||||
all_text_items.sort(key=lambda x: (x["page"], x["y0"], x["x0"]))
|
||||
|
||||
return all_text_items
|
||||
|
||||
def reconstruct_lines(self, text_items: List[Dict]) -> List[Dict]:
|
||||
"""
|
||||
重构文本行，合并同一行的文本片段
|
||||
"""
|
||||
lines = []
|
||||
current_line = None
|
||||
|
||||
for item in text_items:
|
||||
# 如果是新行或者第一个项目
|
||||
if (current_line is None or
|
||||
item["page"] != current_line["page"] or
|
||||
abs(item["y0"] - current_line["y0"]) > 3): # 减小容差到3像素
|
||||
|
||||
# 保存上一行
|
||||
if current_line:
|
||||
lines.append(current_line)
|
||||
|
||||
# 开始新行
|
||||
current_line = {
|
||||
"page": item["page"],
|
||||
"y0": item["y0"],
|
||||
"x0": item["x0"],
|
||||
"text": item["text"],
|
||||
"items": [item]
|
||||
}
|
||||
else:
|
||||
# 继续当前行 - 检查是否需要空格
|
||||
last_item = current_line["items"][-1] if current_line["items"] else None
|
||||
gap = item["x0"] - (last_item["x1"] if last_item else 0)
|
||||
separator = " " if gap > 5 else "" # 如果间隔大于5像素才加空格
|
||||
current_line["text"] += separator + item["text"]
|
||||
current_line["items"].append(item)
|
||||
current_line["x0"] = min(current_line["x0"], item["x0"])
|
||||
|
||||
# 保存最后一行
|
||||
if current_line:
|
||||
lines.append(current_line)
|
||||
|
||||
return lines
|
||||
|
||||
def extract_options_improved(self, question_text: str, text_items: List[Dict]) -> Tuple[str, List[Dict]]:
|
||||
"""
|
||||
改进的选项提取，处理数学符号缺失问题
|
||||
"""
|
||||
lines = question_text.splitlines()
|
||||
options = []
|
||||
stem_buffer = []
|
||||
|
||||
# 首先尝试标准方法
|
||||
current_option = None
|
||||
|
||||
for line in lines:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
|
||||
# 检测选项标签
|
||||
option_match = self.option_line_begin.match(line)
|
||||
if option_match:
|
||||
# 保存前一个选项
|
||||
if current_option:
|
||||
options.append(current_option)
|
||||
|
||||
label = option_match.group(1)
|
||||
text = option_match.group(2).strip()
|
||||
|
||||
# 如果选项文本为空或很短，尝试从坐标信息中补充
|
||||
if not text or len(text) < 2:
|
||||
text = self.find_option_content_by_coords(label, text_items)
|
||||
|
||||
current_option = {"label": label, "text": text}
|
||||
else:
|
||||
# 如果当前在处理选项，继续添加到选项内容
|
||||
if current_option:
|
||||
current_option["text"] += " " + line
|
||||
else:
|
||||
stem_buffer.append(line)
|
||||
|
||||
# 保存最后一个选项
|
||||
if current_option:
|
||||
options.append(current_option)
|
||||
|
||||
# 如果没有找到选项，尝试从完整文本中解析
|
||||
if not options:
|
||||
options = self.extract_options_fallback(question_text)
|
||||
|
||||
# 清理题干
|
||||
stem = "\n".join(stem_buffer).strip()
|
||||
if not stem:
|
||||
# 从完整文本中移除选项部分作为题干
|
||||
stem = self.extract_stem_from_full_text(question_text, options)
|
||||
|
||||
return stem, options
|
||||
|
||||
def find_option_content_by_coords(self, label: str, text_items: List[Dict]) -> str:
|
||||
"""
|
||||
通过坐标信息查找选项内容，用于处理符号缺失
|
||||
"""
|
||||
# 查找选项标签的位置
|
||||
label_item = None
|
||||
for item in text_items:
|
||||
if f"{label}." in item["text"] or f"{label}．" in item["text"]:
|
||||
label_item = item
|
||||
break
|
||||
|
||||
if not label_item:
|
||||
return ""
|
||||
|
||||
# 查找同一行或邻近的其他文本
|
||||
content_items = []
|
||||
for item in text_items:
|
||||
if (item["page"] == label_item["page"] and
|
||||
abs(item["y0"] - label_item["y0"]) <= 3 and # 同一行
|
||||
item["x0"] > label_item["x1"]): # 在标签右侧
|
||||
content_items.append(item)
|
||||
|
||||
# 按x坐标排序并合并
|
||||
content_items.sort(key=lambda x: x["x0"])
|
||||
content = " ".join([item["text"] for item in content_items])
|
||||
|
||||
return content.strip()
|
||||
|
||||
def extract_options_fallback(self, question_text: str) -> List[Dict]:
|
||||
"""
|
||||
备用选项提取方法
|
||||
"""
|
||||
options = []
|
||||
|
||||
# 分割选项的方法：查找A. B. C. D.模式
|
||||
pattern = r"([A-D])\s*[\.．]\s*([^A-D]*?)(?=\s*[A-D]\s*[\.．]|$)"
|
||||
matches = re.findall(pattern, question_text, re.DOTALL)
|
||||
|
||||
for label, content in matches:
|
||||
content = re.sub(r'\s+', ' ', content.strip())
|
||||
if content:
|
||||
options.append({"label": label, "text": content})
|
||||
|
||||
return options
|
||||
|
||||
def extract_stem_from_full_text(self, question_text: str, options: List[Dict]) -> str:
|
||||
"""
|
||||
从完整文本中提取题干
|
||||
"""
|
||||
stem = question_text
|
||||
|
||||
# 移除题号前缀
|
||||
stem = re.sub(r'^\s*\d+\s*[\.．]\s*\([^)]*\)\s*', '', stem)
|
||||
|
||||
# 移除选项内容
|
||||
for option in options:
|
||||
pattern = f"{option['label']}\\s*[\\.．]\\s*{re.escape(option['text'])}"
|
||||
stem = re.sub(pattern, '', stem, flags=re.IGNORECASE)
|
||||
|
||||
# 清理多余空白
|
||||
stem = re.sub(r'\s+', ' ', stem).strip()
|
||||
|
||||
return stem
|
||||
|
||||
def process_pdf(self, pdf_path: str) -> Dict:
|
||||
"""
|
||||
主处理函数
|
||||
"""
|
||||
print(f"🎯 开始修复版PDF解析: {pdf_path}")
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# 1. 提取带坐标的文本
|
||||
text_items = self.extract_text_with_coords(pdf_path)
|
||||
print(f"📝 提取到{len(text_items)}个文本片段")
|
||||
|
||||
# 2. 重构文本行
|
||||
lines = self.reconstruct_lines(text_items)
|
||||
print(f"📄 重构为{len(lines)}行文本")
|
||||
|
||||
# 3. 简化题目识别
|
||||
questions = []
|
||||
current_section = "未知"
|
||||
current_question = None
|
||||
|
||||
for line in lines:
|
||||
text = self.normalize_text(line["text"])
|
||||
|
||||
# 检测分区
|
||||
heading_match = self.heading_pattern.match(text)
|
||||
if heading_match:
|
||||
current_section = f"{heading_match.group(1)}、{heading_match.group(2)}"
|
||||
print(f"📚 发现分区: {current_section}")
|
||||
continue
|
||||
|
||||
# 检测题号
|
||||
question_match = self.question_start_pattern.match(text)
|
||||
if question_match:
|
||||
# 保存上一题
|
||||
if current_question:
|
||||
questions.append(current_question)
|
||||
|
||||
question_num = question_match.group(1)
|
||||
print(f"📝 发现题目: {question_num}")
|
||||
|
||||
# 收集这道题的所有文本（包括后续行直到下一题）
|
||||
question_text = text
|
||||
question_items = line["items"]
|
||||
|
||||
current_question = {
|
||||
"number": question_num,
|
||||
"section": current_section,
|
||||
"text": question_text,
|
||||
"items": question_items,
|
||||
"page": line["page"]
|
||||
}
|
||||
elif current_question:
|
||||
# 继续当前题目
|
||||
current_question["text"] += "\n" + text
|
||||
current_question["items"].extend(line["items"])
|
||||
|
||||
# 保存最后一题
|
||||
if current_question:
|
||||
questions.append(current_question)
|
||||
|
||||
# 4. 处理每道题的选项
|
||||
formatted_questions = []
|
||||
for q in questions:
|
||||
prefer_choice = "选择" in q["section"]
|
||||
|
||||
if prefer_choice:
|
||||
stem, options = self.extract_options_improved(q["text"], q["items"])
|
||||
|
||||
# 转换选项格式
|
||||
options_dict = {}
|
||||
for opt in options:
|
||||
options_dict[opt["label"]] = opt["text"] if opt["text"] else f"[数学符号-{opt['label']}]"
|
||||
else:
|
||||
stem = q["text"]
|
||||
options_dict = None
|
||||
|
||||
# 推断题目类型
|
||||
question_type = "subjective"
|
||||
if "选择" in q["section"]:
|
||||
question_type = "single_choice"
|
||||
elif "填空" in q["section"]:
|
||||
question_type = "fill_blank"
|
||||
|
||||
formatted_questions.append({
|
||||
'number': q['number'],
|
||||
'type': question_type,
|
||||
'text': stem,
|
||||
'options': options_dict,
|
||||
'page': q['page'],
|
||||
'bbox': [],
|
||||
'confidence': 1.0,
|
||||
'page_spans': [q['page']],
|
||||
'source_text': stem,
|
||||
'images': [],
|
||||
'auto_extracted': True,
|
||||
'section': q['section']
|
||||
})
|
||||
|
||||
total_time = time.time() - start_time
|
||||
print(f"🎉 修复版解析完成！总耗时: {total_time:.2f}秒")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"questions": formatted_questions,
|
||||
"total_time": total_time,
|
||||
"pages_processed": max([line["page"] for line in lines]) if lines else 0,
|
||||
"questions_found": len(formatted_questions),
|
||||
"stats": {
|
||||
"total_time": total_time,
|
||||
"pages_processed": max([line["page"] for line in lines]) if lines else 0,
|
||||
"questions_found": len(formatted_questions),
|
||||
"avg_time_per_page": total_time / max([line["page"] for line in lines]) if lines else 0,
|
||||
"method": "fixed_text_parsing"
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 修复版解析失败: {str(e)}")
|
||||
return {"success": False, "error": str(e)}
|
||||
|
||||
# 测试函数
|
||||
def test_fixed_parser():
|
||||
"""
|
||||
测试修复版解析器
|
||||
"""
|
||||
import os
|
||||
|
||||
pdf_path = "/Users/brunogao/Downloads/深圳市八年级（上）期中数学试卷1.pdf"
|
||||
|
||||
if not os.path.exists(pdf_path):
|
||||
print(f"❌ 测试文件不存在: {pdf_path}")
|
||||
return
|
||||
|
||||
parser = PDFExamParserFixed()
|
||||
result = parser.process_pdf(pdf_path)
|
||||
|
||||
if result["success"]:
|
||||
print(f"""
|
||||
📊 修复版解析测试结果:
|
||||
- 总时间: {result['total_time']:.2f}秒
|
||||
- 页数: {result['pages_processed']}
|
||||
- 题目: {result['questions_found']}道
|
||||
- 方法: {result['stats']['method']}
|
||||
""")
|
||||
|
||||
print("前3道选择题详细信息:")
|
||||
for i, q in enumerate(result['questions'][:3]):
|
||||
if q.get('type') == 'single_choice':
|
||||
print(f"\\n题目{q['number']}:")
|
||||
print(f"题干: {q['text']}")
|
||||
if q.get('options'):
|
||||
print(f"选项:")
|
||||
for k, v in q['options'].items():
|
||||
print(f" {k}. {v}")
|
||||
else:
|
||||
print(f"❌ 测试失败: {result['error']}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_fixed_parser()
|
||||
@@ -0,0 +1,184 @@
|
||||
"""
|
||||
混合PDF拆分器：文本层解析 + OCR备份
|
||||
优先使用文本层解析（快速准确），OCR作为扫描版PDF的备份方案
|
||||
"""
|
||||
|
||||
import os
|
||||
import time
|
||||
from typing import Dict, List
|
||||
from pdf_exam_parser_enhanced import PDFExamParserEnhanced
|
||||
from pdf_exam_parser_fixed import PDFExamParserFixed
|
||||
from pdf_splitter_improved import PDFSplitterImproved
|
||||
|
||||
class PDFSplitterHybrid:
|
||||
def __init__(self):
|
||||
self.text_parser = PDFExamParserEnhanced()
|
||||
self.fixed_parser = PDFExamParserFixed()
|
||||
self.ocr_parser = PDFSplitterImproved()
|
||||
|
||||
def has_extractable_text(self, pdf_path: str) -> bool:
|
||||
"""
|
||||
检查PDF是否包含可提取的文本层
|
||||
"""
|
||||
try:
|
||||
import fitz
|
||||
doc = fitz.open(pdf_path)
|
||||
|
||||
# 检查前几页是否有文本
|
||||
text_found = False
|
||||
max_pages_to_check = min(3, len(doc))
|
||||
|
||||
for page_num in range(max_pages_to_check):
|
||||
page = doc[page_num]
|
||||
text = page.get_text().strip()
|
||||
|
||||
# 如果找到足够的文本内容，认为有文本层
|
||||
if len(text) > 100: # 至少100个字符
|
||||
text_found = True
|
||||
break
|
||||
|
||||
doc.close()
|
||||
return text_found
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ 检查文本层失败: {e}")
|
||||
return False
|
||||
|
||||
def fix_empty_options(self, pdf_path: str, result: Dict) -> Dict:
|
||||
"""
|
||||
修复空选项问题
|
||||
"""
|
||||
try:
|
||||
import fitz
|
||||
doc = fitz.open(pdf_path)
|
||||
|
||||
for question in result.get("questions", []):
|
||||
if question.get("type") == "single_choice" and question.get("options"):
|
||||
options = question["options"]
|
||||
empty_options = [k for k, v in options.items() if v.strip() == ""]
|
||||
|
||||
if empty_options:
|
||||
# 提取该题目页面的文本
|
||||
page_num = question.get("page", 1) - 1
|
||||
if page_num < len(doc):
|
||||
page = doc[page_num]
|
||||
text = page.get_text()
|
||||
|
||||
# 查找选项模式
|
||||
for opt_label in empty_options:
|
||||
pattern = f"{opt_label}\\s*[.．)]\\s*([^A-D]*?)(?=[A-D]\\s*[.．)]|$)"
|
||||
import re
|
||||
match = re.search(pattern, text, re.DOTALL)
|
||||
if match:
|
||||
content = match.group(1).strip()
|
||||
if content:
|
||||
options[opt_label] = content
|
||||
print(f"🔧 修复选项{opt_label}: {content[:30]}...")
|
||||
else:
|
||||
# 备用方案：标记为数学符号
|
||||
options[opt_label] = f"[数学符号-{opt_label}]"
|
||||
print(f"⚠️ 无法修复选项{opt_label}，标记为数学符号")
|
||||
|
||||
doc.close()
|
||||
|
||||
except Exception as e:
|
||||
print(f"⚠️ 选项修复失败: {e}")
|
||||
|
||||
return result
|
||||
|
||||
def process_pdf(self, pdf_path: str, output_dir: str = None) -> Dict:
|
||||
"""
|
||||
混合处理策略：优先文本层，后备OCR
|
||||
"""
|
||||
total_start = time.time()
|
||||
print(f"🎯 开始混合PDF处理: {pdf_path}")
|
||||
|
||||
try:
|
||||
# 1. 检查是否有可提取的文本层
|
||||
has_text = self.has_extractable_text(pdf_path)
|
||||
|
||||
if has_text:
|
||||
print("✅ 检测到文本层，使用增强文本解析")
|
||||
result = self.text_parser.process_pdf(pdf_path)
|
||||
|
||||
# 验证文本解析结果质量
|
||||
if (result.get("success") and
|
||||
result.get("questions_found", 0) > 0):
|
||||
|
||||
print(f"🎉 增强文本层解析成功: {result['questions_found']}道题")
|
||||
result["stats"]["method"] = "enhanced_text_parsing"
|
||||
|
||||
# 检查选择题选项是否有数学符号问题
|
||||
choice_questions = [q for q in result.get("questions", []) if q.get("type") == "single_choice"]
|
||||
has_empty_options = any(
|
||||
any(opt.strip() == "" for opt in (q.get("options", {}).values()))
|
||||
for q in choice_questions
|
||||
)
|
||||
|
||||
if has_empty_options:
|
||||
print("⚠️ 检测到选择题选项有空值，尝试选项修复")
|
||||
# 尝试修复空选项
|
||||
result = self.fix_empty_options(pdf_path, result)
|
||||
result["stats"]["method"] = "enhanced_text_parsing_with_fix"
|
||||
|
||||
return result
|
||||
else:
|
||||
print("⚠️ 增强文本层解析结果不理想，回退到OCR")
|
||||
else:
|
||||
print("⚠️ 未检测到文本层，直接使用OCR")
|
||||
|
||||
# 2. 使用OCR作为备份或主要方案
|
||||
print("🔄 使用OCR解析")
|
||||
result = self.ocr_parser.process_pdf(pdf_path, output_dir)
|
||||
|
||||
if result.get("success"):
|
||||
result["stats"]["method"] = "ocr_backup" if has_text else "ocr_primary"
|
||||
print(f"✅ OCR解析完成: {result.get('questions_found', 0)}道题")
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 混合处理失败: {str(e)}")
|
||||
return {"success": False, "error": str(e)}
|
||||
|
||||
# 测试函数
|
||||
def test_hybrid_parser():
|
||||
"""
|
||||
测试混合解析器
|
||||
"""
|
||||
pdf_path = "/Users/brunogao/Downloads/深圳市八年级（上）期中数学试卷1.pdf"
|
||||
|
||||
if not os.path.exists(pdf_path):
|
||||
print(f"❌ 测试文件不存在: {pdf_path}")
|
||||
return
|
||||
|
||||
parser = PDFSplitterHybrid()
|
||||
result = parser.process_pdf(pdf_path)
|
||||
|
||||
if result["success"]:
|
||||
print(f"""
|
||||
📊 混合解析测试结果:
|
||||
- 总时间: {result['total_time']:.2f}秒
|
||||
- 页数: {result['pages_processed']}
|
||||
- 题目: {result['questions_found']}道
|
||||
- 方法: {result['stats']['method']}
|
||||
""")
|
||||
|
||||
print("前3道选择题详细:")
|
||||
choice_count = 0
|
||||
for q in result['questions']:
|
||||
if q.get('type') == 'single_choice' and choice_count < 3:
|
||||
choice_count += 1
|
||||
print(f"\\n题目{q['number']}:")
|
||||
print(f"题干: {q['text']}")
|
||||
if q.get('options'):
|
||||
print("选项:")
|
||||
for k, v in q['options'].items():
|
||||
print(f" {k}. {v}")
|
||||
elif choice_count >= 3:
|
||||
break
|
||||
else:
|
||||
print(f"❌ 测试失败: {result['error']}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_hybrid_parser()
|
||||
@@ -0,0 +1,442 @@
|
||||
"""
|
||||
基于PDF文本层的智能题目解析器
|
||||
借鉴ChatGPT的策略：直接提取PDF文本层，避免OCR误差
|
||||
"""
|
||||
|
||||
import re
|
||||
import json
|
||||
import time
|
||||
from typing import List, Dict, Tuple, Optional
|
||||
import fitz # PyMuPDF
|
||||
import pdfplumber
|
||||
from dataclasses import dataclass
|
||||
|
||||
@dataclass
|
||||
class Question:
|
||||
section: str
|
||||
qno: str
|
||||
type: str
|
||||
stem: str
|
||||
options: Dict[str, str]
|
||||
page_start: int
|
||||
page_end: int
|
||||
confidence: float = 1.0
|
||||
|
||||
class PDFTextParser:
|
||||
def __init__(self):
|
||||
# 大题分区正则
|
||||
self.section_patterns = [
|
||||
re.compile(r'^\s*[（(]?([一二三四五六七八九十]{1,3})[、.)]\s*(\S*题)', re.MULTILINE),
|
||||
re.compile(r'^\s*(\d+)\s*[、.]\s*(\S*题)', re.MULTILINE),
|
||||
]
|
||||
|
||||
# 题号正则 - 支持多种格式
|
||||
self.question_patterns = [
|
||||
re.compile(r'^\s*(?:第\s*)?([0-9]{1,3}|[一二三四五六七八九十]{1,3})\s*[.)]'),
|
||||
re.compile(r'^\s*([0-9]{1,3})\s*[．、]\s*'),
|
||||
re.compile(r'^\s*\(([0-9]{1,3})\)\s*'),
|
||||
re.compile(r'^\s*（([0-9]{1,3})）\s*'),
|
||||
]
|
||||
|
||||
# 选项正则
|
||||
self.option_patterns = [
|
||||
re.compile(r'^\s*([A-H])\s*[.)]\s*(.*)'),
|
||||
re.compile(r'^\s*（([A-H])）\s*(.*)'),
|
||||
re.compile(r'^\s*\(([A-H])\)\s*(.*)'),
|
||||
]
|
||||
|
||||
# 中文数字转换
|
||||
self.chinese_numbers = {
|
||||
'一': '1', '二': '2', '三': '3', '四': '4', '五': '5',
|
||||
'六': '6', '七': '7', '八': '8', '九': '9', '十': '10',
|
||||
'十一': '11', '十二': '12', '十三': '13', '十四': '14', '十五': '15',
|
||||
'十六': '16', '十七': '17', '十八': '18', '十九': '19', '二十': '20'
|
||||
}
|
||||
|
||||
def extract_text_with_pymupdf(self, pdf_path: str) -> List[Dict]:
|
||||
"""
|
||||
使用PyMuPDF提取文本（优先，速度快，顺序准确）
|
||||
"""
|
||||
try:
|
||||
doc = fitz.open(pdf_path)
|
||||
pages_text = []
|
||||
|
||||
for page_num in range(len(doc)):
|
||||
page = doc[page_num]
|
||||
text = page.get_text()
|
||||
|
||||
# 按行分割并保持行信息
|
||||
lines = []
|
||||
for line in text.split('\n'):
|
||||
line = line.strip()
|
||||
if line: # 过滤空行
|
||||
lines.append(line)
|
||||
|
||||
pages_text.append({
|
||||
'page_num': page_num + 1,
|
||||
'lines': lines,
|
||||
'full_text': text
|
||||
})
|
||||
|
||||
doc.close()
|
||||
print(f"✅ PyMuPDF提取完成: {len(pages_text)}页")
|
||||
return pages_text
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ PyMuPDF提取失败: {e}")
|
||||
return []
|
||||
|
||||
def extract_text_with_pdfplumber(self, pdf_path: str) -> List[Dict]:
|
||||
"""
|
||||
使用pdfplumber提取文本（备用，处理复杂布局）
|
||||
"""
|
||||
try:
|
||||
pages_text = []
|
||||
|
||||
with pdfplumber.open(pdf_path) as pdf:
|
||||
for page_num, page in enumerate(pdf.pages):
|
||||
text = page.extract_text()
|
||||
|
||||
if text:
|
||||
lines = []
|
||||
for line in text.split('\n'):
|
||||
line = line.strip()
|
||||
if line:
|
||||
lines.append(line)
|
||||
|
||||
pages_text.append({
|
||||
'page_num': page_num + 1,
|
||||
'lines': lines,
|
||||
'full_text': text
|
||||
})
|
||||
|
||||
print(f"✅ pdfplumber提取完成: {len(pages_text)}页")
|
||||
return pages_text
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ pdfplumber提取失败: {e}")
|
||||
return []
|
||||
|
||||
def extract_pdf_text(self, pdf_path: str) -> List[Dict]:
|
||||
"""
|
||||
智能文本提取：优先PyMuPDF，失败时回退pdfplumber
|
||||
"""
|
||||
print(f"🔄 开始PDF文本提取: {pdf_path}")
|
||||
start_time = time.time()
|
||||
|
||||
# 优先使用PyMuPDF
|
||||
pages_text = self.extract_text_with_pymupdf(pdf_path)
|
||||
|
||||
# 如果PyMuPDF失败或结果为空，回退到pdfplumber
|
||||
if not pages_text:
|
||||
print("⚠️ PyMuPDF结果为空，回退到pdfplumber")
|
||||
pages_text = self.extract_text_with_pdfplumber(pdf_path)
|
||||
|
||||
print(f"⚡ 文本提取完成: 耗时{time.time() - start_time:.2f}秒")
|
||||
return pages_text
|
||||
|
||||
def normalize_text(self, text: str) -> str:
|
||||
"""
|
||||
文本标准化：处理全角半角、中文数字等
|
||||
"""
|
||||
# 全角转半角
|
||||
text = text.replace('（', '(').replace('）', ')')
|
||||
text = text.replace('．', '.').replace('、', '.')
|
||||
text = text.replace('：', ':').replace('；', ';')
|
||||
|
||||
# 去除多余空格
|
||||
text = re.sub(r'\s+', ' ', text).strip()
|
||||
|
||||
return text
|
||||
|
||||
def detect_sections(self, pages_text: List[Dict]) -> List[Dict]:
|
||||
"""
|
||||
检测大题分区（一、选择题，二、填空题等）
|
||||
"""
|
||||
sections = []
|
||||
current_section = None
|
||||
|
||||
for page_data in pages_text:
|
||||
page_num = page_data['page_num']
|
||||
|
||||
for line in page_data['lines']:
|
||||
normalized_line = self.normalize_text(line)
|
||||
|
||||
# 检测分区标题
|
||||
for pattern in self.section_patterns:
|
||||
match = pattern.match(normalized_line)
|
||||
if match:
|
||||
section_num = match.group(1)
|
||||
section_name = match.group(2)
|
||||
|
||||
# 转换中文数字
|
||||
if section_num in self.chinese_numbers:
|
||||
section_num = self.chinese_numbers[section_num]
|
||||
|
||||
current_section = {
|
||||
'section_num': section_num,
|
||||
'section_name': section_name,
|
||||
'start_page': page_num,
|
||||
'start_line': normalized_line
|
||||
}
|
||||
sections.append(current_section)
|
||||
print(f"📚 检测到分区: {section_num}、{section_name} (第{page_num}页)")
|
||||
break
|
||||
|
||||
return sections
|
||||
|
||||
def convert_chinese_number(self, num_str: str) -> str:
|
||||
"""
|
||||
转换中文数字为阿拉伯数字
|
||||
"""
|
||||
if num_str in self.chinese_numbers:
|
||||
return self.chinese_numbers[num_str]
|
||||
return num_str
|
||||
|
||||
def detect_question_number(self, line: str) -> Optional[str]:
|
||||
"""
|
||||
检测题号
|
||||
"""
|
||||
normalized_line = self.normalize_text(line)
|
||||
|
||||
for pattern in self.question_patterns:
|
||||
match = pattern.match(normalized_line)
|
||||
if match:
|
||||
qno = match.group(1)
|
||||
# 转换中文数字
|
||||
qno = self.convert_chinese_number(qno)
|
||||
try:
|
||||
# 验证是否为合理的题号
|
||||
num = int(qno)
|
||||
if 1 <= num <= 100: # 假设题目数量在1-100之间
|
||||
return str(num)
|
||||
except ValueError:
|
||||
continue
|
||||
|
||||
return None
|
||||
|
||||
def detect_option(self, line: str) -> Optional[Tuple[str, str]]:
|
||||
"""
|
||||
检测选项（A、B、C、D等）
|
||||
"""
|
||||
normalized_line = self.normalize_text(line)
|
||||
|
||||
for pattern in self.option_patterns:
|
||||
match = pattern.match(normalized_line)
|
||||
if match:
|
||||
option_letter = match.group(1)
|
||||
option_content = match.group(2).strip()
|
||||
return option_letter, option_content
|
||||
|
||||
return None
|
||||
|
||||
def parse_questions(self, pages_text: List[Dict]) -> List[Question]:
|
||||
"""
|
||||
解析题目：核心算法
|
||||
"""
|
||||
print("🔄 开始智能题目解析")
|
||||
start_time = time.time()
|
||||
|
||||
questions = []
|
||||
current_question = None
|
||||
current_section = "未知"
|
||||
all_lines = []
|
||||
|
||||
# 合并所有页面的行，保持页码信息
|
||||
for page_data in pages_text:
|
||||
for line in page_data['lines']:
|
||||
all_lines.append({
|
||||
'text': line,
|
||||
'page': page_data['page_num']
|
||||
})
|
||||
|
||||
i = 0
|
||||
while i < len(all_lines):
|
||||
line_data = all_lines[i]
|
||||
line = line_data['text']
|
||||
page_num = line_data['page']
|
||||
|
||||
# 检测分区
|
||||
for pattern in self.section_patterns:
|
||||
match = pattern.match(self.normalize_text(line))
|
||||
if match:
|
||||
section_name = match.group(2)
|
||||
current_section = section_name
|
||||
print(f"📚 进入分区: {section_name}")
|
||||
break
|
||||
|
||||
# 检测题号
|
||||
qno = self.detect_question_number(line)
|
||||
if qno:
|
||||
# 保存上一题
|
||||
if current_question:
|
||||
questions.append(current_question)
|
||||
|
||||
# 开始新题
|
||||
question_type = self.infer_question_type(current_section, line)
|
||||
current_question = Question(
|
||||
section=current_section,
|
||||
qno=qno,
|
||||
type=question_type,
|
||||
stem=line,
|
||||
options={},
|
||||
page_start=page_num,
|
||||
page_end=page_num
|
||||
)
|
||||
print(f"📝 题目{qno}: {line[:50]}...")
|
||||
|
||||
elif current_question:
|
||||
# 检测选项
|
||||
option_result = self.detect_option(line)
|
||||
if option_result:
|
||||
option_letter, option_content = option_result
|
||||
current_question.options[option_letter] = option_content
|
||||
current_question.page_end = page_num
|
||||
else:
|
||||
# 继续当前题目内容
|
||||
if len(current_question.stem) < 1000: # 防止过长
|
||||
current_question.stem += ' ' + line
|
||||
current_question.page_end = page_num
|
||||
|
||||
i += 1
|
||||
|
||||
# 保存最后一题
|
||||
if current_question:
|
||||
questions.append(current_question)
|
||||
|
||||
print(f"✅ 题目解析完成: {len(questions)}道题, 耗时{time.time() - start_time:.2f}秒")
|
||||
return questions
|
||||
|
||||
def infer_question_type(self, section: str, line: str) -> str:
|
||||
"""
|
||||
推断题目类型
|
||||
"""
|
||||
if '选择' in section:
|
||||
return 'multiple_choice'
|
||||
elif '填空' in section:
|
||||
return 'fill_blank'
|
||||
elif '解答' in section or '计算' in section:
|
||||
return 'solve'
|
||||
elif '判断' in section:
|
||||
return 'true_false'
|
||||
else:
|
||||
# 根据内容推断
|
||||
if re.search(r'[A-D][.)）]', line):
|
||||
return 'multiple_choice'
|
||||
elif '______' in line or '___' in line:
|
||||
return 'fill_blank'
|
||||
else:
|
||||
return 'unknown'
|
||||
|
||||
def process_pdf(self, pdf_path: str) -> Dict:
|
||||
"""
|
||||
主处理函数
|
||||
"""
|
||||
total_start = time.time()
|
||||
print(f"🎯 开始基于文本层的PDF解析: {pdf_path}")
|
||||
|
||||
try:
|
||||
# 1. 提取PDF文本
|
||||
pages_text = self.extract_pdf_text(pdf_path)
|
||||
|
||||
if not pages_text:
|
||||
return {"success": False, "error": "PDF文本提取失败"}
|
||||
|
||||
# 2. 解析题目
|
||||
questions = self.parse_questions(pages_text)
|
||||
|
||||
# 3. 转换为API格式
|
||||
formatted_questions = []
|
||||
for q in questions:
|
||||
formatted_questions.append({
|
||||
'number': q.qno,
|
||||
'type': q.type,
|
||||
'text': q.stem,
|
||||
'options': q.options if q.options else None,
|
||||
'page': q.page_start,
|
||||
'bbox': [], # 文本层解析无bbox信息
|
||||
'confidence': q.confidence,
|
||||
'page_spans': list(range(q.page_start, q.page_end + 1)),
|
||||
'source_text': q.stem,
|
||||
'images': [],
|
||||
'auto_extracted': True,
|
||||
'section': q.section
|
||||
})
|
||||
|
||||
total_time = time.time() - total_start
|
||||
print(f"🎉 文本层解析完成！总耗时: {total_time:.2f}秒")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"questions": formatted_questions,
|
||||
"total_time": total_time,
|
||||
"pages_processed": len(pages_text),
|
||||
"questions_found": len(formatted_questions),
|
||||
"stats": {
|
||||
"total_time": total_time,
|
||||
"pages_processed": len(pages_text),
|
||||
"questions_found": len(formatted_questions),
|
||||
"avg_time_per_page": total_time / len(pages_text) if pages_text else 0,
|
||||
"method": "text_layer_parsing"
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 文本层解析失败: {str(e)}")
|
||||
return {"success": False, "error": str(e)}
|
||||
|
||||
def export_to_json(self, questions: List[Question], output_path: str):
|
||||
"""
|
||||
导出为JSON格式
|
||||
"""
|
||||
data = []
|
||||
for q in questions:
|
||||
data.append({
|
||||
'section': q.section,
|
||||
'qno': q.qno,
|
||||
'type': q.type,
|
||||
'stem': q.stem,
|
||||
'options': q.options,
|
||||
'page_start': q.page_start,
|
||||
'page_end': q.page_end,
|
||||
'confidence': q.confidence
|
||||
})
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
print(f"✅ JSON导出完成: {output_path}")
|
||||
|
||||
# 测试函数
|
||||
def test_text_parser():
|
||||
"""
|
||||
测试文本解析器
|
||||
"""
|
||||
pdf_path = "/Users/brunogao/Downloads/深圳市八年级（上）期中数学试卷1.pdf"
|
||||
|
||||
if not os.path.exists(pdf_path):
|
||||
print(f"❌ 测试文件不存在: {pdf_path}")
|
||||
return
|
||||
|
||||
parser = PDFTextParser()
|
||||
result = parser.process_pdf(pdf_path)
|
||||
|
||||
if result["success"]:
|
||||
print(f"""
|
||||
📊 文本层解析测试结果:
|
||||
- 总时间: {result['total_time']:.2f}秒
|
||||
- 页数: {result['pages_processed']}
|
||||
- 题目: {result['questions_found']}道
|
||||
- 方法: {result['stats']['method']}
|
||||
""")
|
||||
|
||||
print("识别的题目:")
|
||||
for i, q in enumerate(result['questions'][:8]):
|
||||
options_str = f" 选项: {list(q.get('options', {}).keys())}" if q.get('options') else ""
|
||||
print(f"题目{q['number']} ({q['type']}): {q['text'][:60]}...{options_str}")
|
||||
else:
|
||||
print(f"❌ 测试失败: {result['error']}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
import os
|
||||
test_text_parser()
|
||||
@@ -4,7 +4,7 @@ import { API_BASE_URL } from '../config'
|
||||
|
||||
const api = axios.create({
|
||||
baseURL: API_BASE_URL,
|
||||
timeout: 10000,
|
||||
timeout: 30000,
|
||||
headers: {
|
||||
'Content-Type': 'application/json'
|
||||
}
|
||||
|
||||
@@ -19,6 +19,9 @@
|
||||
<el-button @click="showExtractDialog = true" icon="DocumentAdd" type="warning">
|
||||
提取错题
|
||||
</el-button>
|
||||
<el-button @click="splitPaper" icon="Scissor" type="info" :loading="splitting">
|
||||
拆分题目
|
||||
</el-button>
|
||||
</div>
|
||||
</div>
|
||||
</el-header>
|
||||
@@ -244,8 +247,8 @@
|
||||
<script setup>
|
||||
import { ref, reactive, onMounted, computed } from 'vue'
|
||||
import { useRouter, useRoute } from 'vue-router'
|
||||
import { ElMessage } from 'element-plus'
|
||||
import { ArrowLeft, Download, DocumentAdd, Reading } from '@element-plus/icons-vue'
|
||||
import { ElMessage, ElMessageBox, ElNotification } from 'element-plus'
|
||||
import { ArrowLeft, Download, DocumentAdd, Reading, Scissor } from '@element-plus/icons-vue'
|
||||
import api from '../services/api'
|
||||
import { buildFileUrl } from '../config'
|
||||
|
||||
@@ -258,6 +261,7 @@ const mistakes = ref([])
|
||||
const loading = ref(false)
|
||||
const extracting = ref(false)
|
||||
const showExtractDialog = ref(false)
|
||||
const splitting = ref(false)
|
||||
|
||||
const extractForm = reactive({
|
||||
page: 1,
|
||||
@@ -453,6 +457,40 @@ const submitExtract = async () => {
|
||||
}
|
||||
}
|
||||
|
||||
// 拆分试卷为题目
|
||||
const splitPaper = async () => {
|
||||
try {
|
||||
await ElMessageBox.confirm(
|
||||
'拆分试卷将自动识别并分割题目，此过程可能需要几分钟，确定要继续吗？',
|
||||
'确认拆分',
|
||||
{
|
||||
confirmButtonText: '确定拆分',
|
||||
cancelButtonText: '取消',
|
||||
type: 'warning',
|
||||
}
|
||||
)
|
||||
|
||||
splitting.value = true
|
||||
const response = await api.post(`/papers/${paperId.value}/split`)
|
||||
|
||||
if (response.data.success) {
|
||||
ElNotification.success({
|
||||
title: '拆分成功',
|
||||
message: `成功识别 ${response.data.stats.total_questions} 道题目`,
|
||||
duration: 3000
|
||||
})
|
||||
} else {
|
||||
ElMessage.error(`拆分失败: ${response.data.error}`)
|
||||
}
|
||||
} catch (error) {
|
||||
if (error !== 'cancel') {
|
||||
ElMessage.error('拆分失败：' + (error.response?.data?.detail || error.message))
|
||||
}
|
||||
} finally {
|
||||
splitting.value = false
|
||||
}
|
||||
}
|
||||
|
||||
onMounted(() => {
|
||||
fetchPaper()
|
||||
fetchMistakes()
|
||||
|
||||
@@ -10,6 +10,9 @@
|
||||
<h1>{{ paper?.title || '加载中...' }}</h1>
|
||||
</div>
|
||||
<div class="header-actions" v-if="paper">
|
||||
<el-button @click="splitPaper" icon="Scissor" type="info" :loading="splitting">
|
||||
拆分题目
|
||||
</el-button>
|
||||
<el-button @click="showQuestionBank = true" icon="Collection" type="primary">
|
||||
题库管理
|
||||
</el-button>
|
||||
@@ -311,8 +314,8 @@
|
||||
<script setup>
|
||||
import { ref, reactive, onMounted, computed } from 'vue'
|
||||
import { useRouter, useRoute } from 'vue-router'
|
||||
import { ElMessage, ElMessageBox } from 'element-plus'
|
||||
import { ArrowLeft, Download, Document, Search, Collection, Edit, Delete, Plus } from '@element-plus/icons-vue'
|
||||
import { ElMessage, ElMessageBox, ElNotification } from 'element-plus'
|
||||
import { ArrowLeft, Download, Document, Search, Collection, Edit, Delete, Plus, Scissor } from '@element-plus/icons-vue'
|
||||
import api from '../services/api'
|
||||
import { useAuthStore } from '../stores/auth'
|
||||
import { buildFileUrl } from '../config'
|
||||
@@ -326,6 +329,7 @@ const paper = ref(null)
|
||||
const questions = ref([])
|
||||
const loading = ref(false)
|
||||
const saving = ref(false)
|
||||
const splitting = ref(false)
|
||||
const addQuestionMode = ref(false)
|
||||
const showQuestionBank = ref(false)
|
||||
const activeTab = ref('mybank')
|
||||
@@ -501,6 +505,42 @@ const deleteQuestion = async (questionId) => {
|
||||
}
|
||||
}
|
||||
|
||||
// 拆分试卷为题目
|
||||
const splitPaper = async () => {
|
||||
try {
|
||||
await ElMessageBox.confirm(
|
||||
'拆分试卷将自动识别并分割题目，此过程可能需要几分钟，确定要继续吗？',
|
||||
'确认拆分',
|
||||
{
|
||||
confirmButtonText: '确定拆分',
|
||||
cancelButtonText: '取消',
|
||||
type: 'warning',
|
||||
}
|
||||
)
|
||||
|
||||
splitting.value = true
|
||||
const response = await api.post(`/papers/${paperId.value}/split`, {}, { timeout: 300000 })
|
||||
|
||||
if (response.data.success) {
|
||||
ElNotification.success({
|
||||
title: '拆分成功',
|
||||
message: `成功识别 ${response.data.stats.total_questions} 道题目`,
|
||||
duration: 3000
|
||||
})
|
||||
// 刷新题目列表
|
||||
fetchQuestions()
|
||||
} else {
|
||||
ElMessage.error(`拆分失败: ${response.data.error}`)
|
||||
}
|
||||
} catch (error) {
|
||||
if (error !== 'cancel') {
|
||||
ElMessage.error('拆分失败：' + (error.response?.data?.detail || error.message))
|
||||
}
|
||||
} finally {
|
||||
splitting.value = false
|
||||
}
|
||||
}
|
||||
|
||||
const downloadPaper = () => {
|
||||
if (!paper.value || !authStore.token) return
|
||||
const url = buildFileUrl(`papers/${paperId.value}/file`, { access_token: authStore.token })
|
||||
|
||||
Reference in New Issue
Block a user