python - 单个单词的 PDFMiner 提取 - LTText LTTextBox

Question

在下面的示例中，我使用 PDFMiner 生成单词 x,y 坐标，但是结果是逐行生成的。我怎样才能将每个单词从另一个单词中拆分出来，而不是逐行拆分单词组(参见下面的示例)。我已经尝试了 PDFMiner tutorial 中的几个论点。 . LTTextBox 和 LTText 都试过了。此外，我不能使用文本分析中通常使用的开始和结束偏移量。

这个 PDF 是一个很好的例子，它用在下面的代码中。

http://www.africau.edu/images/default/sample.pdf

from pdfminer.layout import LAParams, LTTextBox, LTText
from pdfminer.pdfpage import PDFPage
from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager
from pdfminer.converter import PDFPageAggregator

#Imports Searchable PDFs and prints x,y coordinates
fp = open('C:\sample.pdf', 'rb')
manager = PDFResourceManager()
laparams = LAParams()
dev = PDFPageAggregator(manager, laparams=laparams)
interpreter = PDFPageInterpreter(manager, dev)
pages = PDFPage.get_pages(fp)

for page in pages:
    print('--- Processing ---')
    interpreter.process_page(page)
    layout = dev.get_result()
    for lobj in layout:
        if isinstance(lobj, LTText):
            x, y, text = lobj.bbox[0], lobj.bbox[3], lobj.get_text()
            print('At %r is text: %s' % ((x, y), text))

这将返回可搜索 PDF 的 x,y 坐标，如下所示:

--- Processing ---
At (57.375, 747.903) is text: A Simple PDF File
At (69.25, 698.098) is text: This is a small demonstration .pdf file -
At (69.25, 674.194) is text: just for use in the Virtual Mechanics tutorials. More text. And more 
 text. And more text. And more text. And more text.

想要的结果(坐标为演示的代理):

--- Processing ---
At (57.375, 747.903) is text: A
At (69.25, 698.098) is text: Simple
At (69.25, 674.194) is text: PDF
At (69.25, 638.338) is text: File

Answer 1

使用 PDFMiner，在遍历每一行之后(就像你已经做的那样)，你只能遍历行中的每个字符。

我用下面的代码做到了这一点，同时尝试记录每个单词第一个字符的 x、y 并设置条件以在每个 LTAnno 处拆分单词(例如\n )或.get_text() == ' ' 空格。

from pdfminer.layout import LAParams, LTTextBox, LTText, LTChar, LTAnno
from pdfminer.pdfpage import PDFPage
from pdfminer.pdfinterp import PDFPageInterpreter, PDFResourceManager
from pdfminer.converter import PDFPageAggregator

#Imports Searchable PDFs and prints x,y coordinates
fp = open('C:\sample.pdf', 'rb')
manager = PDFResourceManager()
laparams = LAParams()
dev = PDFPageAggregator(manager, laparams=laparams)
interpreter = PDFPageInterpreter(manager, dev)
pages = PDFPage.get_pages(fp)

for page in pages:
    print('--- Processing ---')
    interpreter.process_page(page)
    layout = dev.get_result()
    x, y, text = -1, -1, ''
    for textbox in layout:
        if isinstance(textbox, LTText):
          for line in textbox:
            for char in line:
              # If the char is a line-break or an empty space, the word is complete
              if isinstance(char, LTAnno) or char.get_text() == ' ':
                if x != -1:
                  print('At %r is text: %s' % ((x, y), text))
                x, y, text = -1, -1, ''     
              elif isinstance(char, LTChar):
                text += char.get_text()
                if x == -1:
                  x, y, = char.bbox[0], char.bbox[3]    
    # If the last symbol in the PDF was neither an empty space nor a LTAnno, print the word here
    if x != -1:
      print('At %r is text: %s' % ((x, y), text))

输出如下所示

At (64.881, 747.903) is text: A
At (90.396, 747.903) is text: Simple
At (180.414, 747.903) is text: PDF
At (241.92, 747.903) is text: File

或许您可以根据您的要求和喜好优化检测词的条件。 (例如，在词尾剪掉标点符号 .!?)