pdfplumber和pdfminer.six提取PDF中文本行内容及对应坐标

引言

• 最近在做PDF文件的解析，对于在PDF阅读器中可以直接复制的PDF文件，同样，也可以由代码直接解析提取出来对应文本
• 经过一系列调研，发现用的最多的两个库为：pdfplumber 和 pdfminer.six
• 以下分别介绍这两个库如何有效提取PDF中文本行内容以及对应坐标
• 示例PDF文件的下载链接

pdfplumber提取方案

• 官方repo: jsvine/pdfplumber
• 说明文档即是该仓库下的README文件
• 运行代码:

  import pdfplumber
  
  pdf_path = 'hung2019.pdf'
  with pdfplumber.open(pdf_path) as pdf:
      first_page = pdf.pages[0]
      result = first_page.extract_words(x_tolerance=1, keep_blank_chars=True)
      for value in result:
          print(value['text'])
  

• 部分输出结果：

  Malware
  detection
  based
  on
  directed
  multi-edge
  dataflow
  graph
  representation
  and
  convolutional
  neural
  network
  

• 由以上结果可见，即使设置了keep_blank_chars=True，仍不能很好提取出每一行内容。不过，还有一些超参数可以调节，例如x_tolerance和y_tolerance等等。我反正是试了好多，都不得行。

✧ pdfminer.six提取方案

• 官方repo
• 官方说明文档 (这个文档似乎只维护了一部分，并没有写完）

比较复杂版本

• 运行代码：

  from pdfminer.pdfparser import PDFParser
  from pdfminer.pdfdocument import PDFDocument
  from pdfminer.pdfpage import PDFPage
  from pdfminer.pdfpage import PDFTextExtractionNotAllowed
  from pdfminer.pdfinterp import PDFResourceManager
  from pdfminer.pdfinterp import PDFPageInterpreter
  from pdfminer.pdfdevice import PDFDevice
  from pdfminer.layout import *
  from pdfminer.converter import PDFPageAggregator
  
  pdf_path = 'hung2019.pdf'
  f = open(pdf_path, 'rb')
  
  #来创建一个pdf文档分析器
  parser = PDFParser(f)
  
  #创建一个PDF文档对象存储文档结构
  document = PDFDocument(parser)
  document.is_extractable
  
  # 创建一个PDF资源管理器对象来存储共赏资源
  rsrcmgr = PDFResourceManager()
  
  # 设定参数进行分析
  laparams = LAParams()
  
  # 创建一个PDF设备对象
  device = PDFPageAggregator(rsrcmgr,laparams=laparams)
  
  # 创建一个PDF解释器对象
  interpreter = PDFPageInterpreter(rsrcmgr,device)
  
  # 处理每一页
  for page in PDFPage.create_pages(document):
      interpreter.process_page(page)
      
      # 接受该页面的LTPage对象
      layout = device.get_result()
      page_height = layout.bbox[3]
      
      for x in layout:
          if isinstance(x, LTTextBox):
              for v in x:
                  if isinstance(v, LTTextLine):
                      text = v.get_text()
                      x0, y0, x1, y1 = v.bbox 
                      
                      # 注意这里的bbox y轴坐标需要用page 高度减去才是 正常坐标
                      y0 = page_height - y0
                      y1 = page_height - y1
                      print(f'{text}\t({x0}, {y0}, {x1}, {y1})')
  f.close()
  

• 部分输出内容

  Malware detection based on directed multi-edge
  	(69.517, 77.95079429999998, 542.4866443, 54.04049429999998)
  dataflow graph representation and convolutional
  	(71.142, 105.84579429999997, 540.8598435, 81.93549429999996)
  neural network
  	(232.883, 133.74179430000004, 379.11839480000003, 109.83149430000003)
  Nguyen Viet Hung
  	(105.702, 161.48445089999996, 190.63347499999998, 150.52555089999998)
  Le Quy Don Techincal University
  	(81.264, 173.5719019999999, 218.55859060000006, 163.60930199999996)
  Faculty of Information Technology
  	(76.013, 185.899902, 216.83435100000005, 175.93730200000005)
  

高阶函数版本

• 运行代码：

  from pdfminer.high_level import extract_pages
  from pdfminer.layout import LTPage, LTTextBoxHorizontal, LTTextLineHorizontal
  
  pdf_path = 'hung2019.pdf'
  pages = list(extract_pages(pdf_path))
  
  # 示例，取第一页
  page = pages[0]
  boxes, texts = [], []
   if isinstance(page, LTPage):
         for text_box_h in page:
             if isinstance(text_box_h, LTTextBoxHorizontal):
                 for text_box_h_l in text_box_h:
                     if isinstance(text_box_h_l, LTTextLineHorizontal):
                         x0, y0, x1, y1 = text_box_h_l.bbox
                         y0 = page.height - y0
                         y1 = page.height - y1
  
                         text = text_box_h_l.get_text()
                         boxes.append([[x0, y0], [x1, y0],
                                            [x1, y1], [x0, y1]])
                         texts.append(text)
                         
                         print(f'{text}\t({x0}, {y0}, {x1}, {y1})')
  

• 部分输出结果：

  Malware detection based on directed multi-edge
     (69.517, 77.95079429999998, 542.4866443, 54.04049429999998)
  dataflow graph representation and convolutional
     (71.142, 105.84579429999997, 540.8598435, 81.93549429999996)
  neural network
     (232.883, 133.74179430000004, 379.11839480000003, 109.83149430000003)
  Nguyen Viet Hung
     (105.702, 161.48445089999996, 190.63347499999998, 150.52555089999998)
  Le Quy Don Techincal University
     (81.264, 173.5719019999999, 218.55859060000006, 163.60930199999996)
  Faculty of Information Technology
     (76.013, 185.899902, 216.83435100000005, 175.93730200000005)
  

总结

• 由以上结果可以看出，pdfminer.six库有着比pdfplumber更加好的效果，同时也更加灵活。

参考文献

• How to extract text and text coordinates from a PDF file?