单词PDFMiner抽取-- 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。我如何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中的几个参数。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，在下面的代码中使用了它。

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(坐标是演示的代理)：

--- 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(就像您已经做过的那样)，您可以只遍历该行中的每个字符。

我使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，并设置一个条件以在每个LTAnno (例如\n )或.get_text() == ' '空格处拆分单词。

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

也许您可以优化条件，以检测符合您的需求和喜好的词语。(例如，删除标点符号。！？在单词末尾)

Answer 2

读者可能想要尝试的另一个包是pdfparser，它也是在Poppler上构建的(使用Cyton绑定)，并且恰好在性能上进行了更优化

                           pdfreader      pdfminer  speed-up factor

tiny document (half page)    0.033s         0.121s     3.6 x
small document (5 pages)     0.141s         0.810s     5.7 x
medium document (55 pages)   1.166s        10.524s     9.0 x
large document (436 pages)  10.581s       108.095s    10.2 x

除了更快之外，它的错误处理也更好，并解决了Pdfminer卡顿的几个问题

import pdfparser.poppler as pdf
import sys

d=pdf.Document(sys.argv[1])

print('No of pages', d.no_of_pages)
for p in d:
    print('Page', p.page_no, 'size =', p.size)
    for f in p:
        print(' '*1,'Flow')
        for b in f:
            print(' '*2,'Block', 'bbox=', b.bbox.as_tuple())
            for l in b:
                print(' '*3, l.text.encode('UTF-8'), '(%0.2f, %0.2f, %0.2f, %0.2f)'% l.bbox.as_tuple())
                #assert l.char_fonts.comp_ratio < 1.0
                for i in range(len(l.text)):
                    print(l.text[i].encode('UTF-8'), '(%0.2f, %0.2f, %0.2f, %0.2f)'% l.char_bboxes[i].as_tuple(),\
                        l.char_fonts[i].name, l.char_fonts[i].size, l.char_fonts[i].color,)
                print()

正如你可以清楚地看到，源代码是最短的，但仍然提供了所有必要的数据，包括字体颜色，字体大小，字体系列。

更重要的是，您可以直接在区块中获取单词9(仅比字符高一级)。避免了空间检查逻辑，必须与Pdfminer一起使用。