How to extract table as text from the PDF using Python

Question

I have a PDF which contains Tables  text and some images  I want to extract the table wherever tables are there in the PDF   Right now am doing manually to find the Table from the page  From there I am capturing that page and saving into another PDF   import PyPDF2  PDFfilename    Sammamish pdf   filename of your PDF directory where your PDF is stored  pfr   PyPDF2 PdfFileReader open PDFfilename   rb     PdfFileReader object  pg4   pfr getPage 126   extract pg 127  writer   PyPDF2 PdfFileWriter    create PdfFileWriter object  add pages writer addPage pg4   NewPDFfilename    allTables pdf   filename of your PDF directory where you want your new PDF to be with open NewPDFfilename   wb   as outputStream      writer write outputStream   write pages to new PDF   My goal is to extract the table from the whole PDF document

User · Answer

If your pdf is text-based and not a scanned document (i.e. if you can click and drag to select text in your table in a PDF viewer), then you can use the module camelot-py with

import camelot
tables = camelot.read_pdf('foo.pdf')

You then can choose how you want to save the tables (as csv, json, excel, html, sqlite), and whether the output should be compressed in a ZIP archive.

tables.export('foo.csv', f='csv', compress=False)

Edit: tabula-py appears roughly 6 times faster than camelot-py so that should be used instead.

import camelot
import cProfile
import pstats
import tabula

cmd_tabula = "tabula.read_pdf('table.pdf', pages='1', lattice=True)"
prof_tabula = cProfile.Profile().run(cmd_tabula)
time_tabula = pstats.Stats(prof_tabula).total_tt

cmd_camelot = "camelot.read_pdf('table.pdf', pages='1', flavor='lattice')"
prof_camelot = cProfile.Profile().run(cmd_camelot)
time_camelot = pstats.Stats(prof_camelot).total_tt

print(time_tabula, time_camelot, time_camelot/time_tabula)

gave

1.8495559890000015 11.057014036000016 5.978199147125147

User · Answer

I would suggest you to extract the table using tabula.
Pass your pdf as an argument to the tabula api and it will return you the table in the form of dataframe.
Each table in your pdf is returned as one dataframe.
The table will be returned in a list of dataframea, for working with dataframe you need pandas.

This is my code for extracting pdf.

import pandas as pd
import tabula
file = "filename.pdf"
path = 'enter your directory path here'  + file
df = tabula.read_pdf(path, pages = '1', multiple_tables = True)
print(df)

Please refer to this repo of mine for more details.

User · Answer

This answer is for anyone encountering pdfs with images and needing to use OCR  I could not find a workable off-the-shelf solution  nothing that gave me the accuracy I needed   Here are the steps I found to work    Use pdfimages from https   poppler freedesktop org  to turn the pages of the pdf into images  Use Tesseract to detect rotation and ImageMagick mogrify to fix it  Use OpenCV to find and extract tables  Use OpenCV to find and extract each cell from the table  Use OpenCV to crop and clean up each cell so that there is no noise that will confuse OCR software  Use Tesseract to OCR each cell  Combine the extracted text of each cell into the format you need    I wrote a python package with modules that can help with those steps   Repo  https   github com eihli image-table-ocr  Docs  amp  Source  https   eihli github io image-table-ocr pdf table extraction and ocr html   Some of the steps don t require code  they take advantage of external tools like pdfimages and tesseract  I ll provide some brief examples for a couple of the steps that do require code    Finding tables    This link was a good reference while figuring out how to find tables  https   answers opencv org question 63847 how-to-extract-tables-from-an-image   import cv2  def find tables image       BLUR KERNEL SIZE    17  17      STD DEV X DIRECTION   0     STD DEV Y DIRECTION   0     blurred   cv2 GaussianBlur image  BLUR KERNEL SIZE  STD DEV X DIRECTION  STD DEV Y DIRECTION      MAX COLOR VAL   255     BLOCK SIZE   15     SUBTRACT FROM MEAN   -2      img bin   cv2 adaptiveThreshold           blurred          MAX COLOR VAL          cv2 ADAPTIVE THRESH MEAN C          cv2 THRESH BINARY          BLOCK SIZE          SUBTRACT FROM MEAN            vertical   horizontal   img bin copy       SCALE   5     image width  image height   horizontal shape     horizontal kernel   cv2 getStructuringElement cv2 MORPH RECT   int image width   SCALE   1       horizontally opened   cv2 morphologyEx img bin  cv2 MORPH OPEN  horizontal kernel      vertical kernel   cv2 getStructuringElement cv2 MORPH RECT   1  int image height   SCALE        vertically opened   cv2 morphologyEx img bin  cv2 MORPH OPEN  vertical kernel       horizontally dilated   cv2 dilate horizontally opened  cv2 getStructuringElement cv2 MORPH RECT   40  1        vertically dilated   cv2 dilate vertically opened  cv2 getStructuringElement cv2 MORPH RECT   1  60         mask   horizontally dilated   vertically dilated     contours  hierarchy   cv2 findContours          mask  cv2 RETR EXTERNAL  cv2 CHAIN APPROX SIMPLE             MIN TABLE AREA   1e5     contours    c for c in contours if cv2 contourArea c   gt  MIN TABLE AREA      perimeter lengths    cv2 arcLength c  True  for c in contours      epsilons    0 1   p for p in perimeter lengths      approx polys    cv2 approxPolyDP c  e  True  for c  e in zip contours  epsilons       bounding rects    cv2 boundingRect a  for a in approx polys         The link where a lot of this code was borrowed from recommends an       additional step to check the number of  joints  inside this bounding rectangle        A table should have a lot of intersections  We might have a rectangular image       here though which would only have 4 intersections  1 at each corner        Leaving that step as a future TODO if it is ever necessary      images    image y y h  x x w  for x  y  w  h in bounding rects      return images    Extract cells from table    This is very similar to 2  so I won t include all the code  The part I will reference will be in sorting the cells   We want to identify the cells from left-to-right  top-to-bottom   We   ll find the rectangle with the most top-left corner  Then we   ll find all of the rectangles that have a center that is within the top-y and bottom-y values of that top-left rectangle  Then we   ll sort those rectangles by the x value of their center  We   ll remove those rectangles from the list and repeat   def cell in same row c1  c2       c1 center   c1 1    c1 3  - c1 3    2     c2 bottom   c2 1    c2 3      c2 top   c2 1      return c2 top  lt  c1 center  lt  c2 bottom  orig cells    c for c in cells  rows      while cells      first   cells 0      rest   cells 1       cells in same row   sorted                        c for c in rest             if cell in same row c  first                     key lambda c  c 0             row cells   sorted  first    cells in same row  key lambda c  c 0       rows append row cells      cells             c for c in rest         if not cell in same row c  first           Sort rows by average height of their center  def avg height of center row       centers    y   h - h   2 for x  y  w  h in row      return sum centers    len centers   rows sort key avg height of center

[python] How to extract table as text from the PDF using Python?

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