How to extract text from a PDF file via python?

Question

I'm trying to extract the text included in this PDF file using Python.

I'm using the PyPDF2 package (version 1.27.2), and have the following script:

import PyPDF2

with open("sample.pdf", "rb") as pdf_file:
    read_pdf = PyPDF2.PdfFileReader(pdf_file)
    number_of_pages = read_pdf.getNumPages()
    page = read_pdf.pages[0]
    page_content = page.extractText()
print(page_content)

When I run the code, I get the following output which is different from that included in the PDF document:

 ! " # $ % # $ % &% $ &' ( ) * % + , - % . / 0 1 ' * 2 3% 4
5
 ' % 1 $ # 2 6 % 3/ % 7 / ) ) / 8 % &) / 2 6 % 8 # 3" % 3" * % 31 3/ 9 # &)
%

How can I extract the text as is in the PDF document?

Answer 1

pypdf recently improved a lot. Depending on the data, it is on-par or better than pdfminer.six.

pymupdf / tika / PDFium are better than pypdf, but the difference became rather small - (mostly when to set a new line). The core part is that they are way faster. But they are not pure-Python which can mean that you cannot execute it. And some might have too restrictive licenses so that you may not use it.

Have a look at the benchmark. This benchmark mainly considers English texts, but also German ones. It does not include:

• Anything special regarding tables (just that the text is there, not about the formatting)
• Arabic test (RTL-languages)
• Mathematical formulas.

That means if your use-case requires those points, you might perceive the quality differently.

Having said that, the results from November 2022:

pypdf

I became the maintainer of pypdf and PyPDF2 in 2022! 😁 The community improved the text extraction a lot in 2022. Give it a try :-)

First, install it:

pip install pypdf

And then use it:

from pypdf import PdfReader

reader = PdfReader("example.pdf")
text = ""
for page in reader.pages:
    text += page.extract_text() + "\n"

Please note that those packages are not maintained:

• PyPDF2, PyPDF3, PyPDF4
• pdfminer (without .six)

pymupdf

import fitz # install using: pip install PyMuPDF

with fitz.open("my.pdf") as doc:
    text = ""
    for page in doc:
        text += page.get_text()

print(text)

Other PDF libraries

• pikepdf does not support text extraction (source)

Answer 2

Disclaimer: I am the author of borb the library used in this answer.

Try out borb, a pure python PDF library

import typing  
from borb.pdf.document import Document  
from borb.pdf.pdf import PDF  
from borb.toolkit.text.simple_text_extraction import SimpleTextExtraction  


def main():

    # variable to hold Document instance
    doc: typing.Optional[Document] = None  

    # this implementation of EventListener handles text-rendering instructions
    l: SimpleTextExtraction = SimpleTextExtraction()  

    # open the document, passing along the array of listeners
    with open("input.pdf", "rb") as in_file_handle:  
        doc = PDF.loads(in_file_handle, [l])  
  
    # were we able to read the document?
    assert doc is not None  

    # print the text on page 0
    print(l.get_text(0))  

if __name__ == "__main__":
    main()

Answer 3

This Question so far has 35 answers and not one seems to mention that
the text extracted is the true text from the Questioners PDF page. Nor explained WHY.

For comparison here is the RAW PDF code when decompressed (inflated, under the surface, by the PDF viewer). Thus in some cases this is what is extractable The native "Literal" plain text.

 BT 50 0 0 50 0 0 Tm /TT2 1 Tf [ (!) -0.3 (") -0.4 (#) -0.5 ($) -0.1 (%)
-0.1 (#) -0.5 ($) -0.1 (%) -0.1 (&%) -0.1 ($) -0.1 (&') 0.2 (\() -0.4 (\))
-0.5 (*) 0.4 (%) -0.1 (+) 0.4 (,) -0.2 (-) -0.5 (%) -0.1 (.) -0.4 (/) -0.3
(0) 0.1 (1) -0.4 (') 0.2 (*) 0.4 (2) -0.4 (3%) -0.1 (4) ] TJ ET

and

BT 50 0 0 50 0 0
Tm /TT2 1 Tf (5) Tj ET

and

BT 50 0 0 50 0 0 Tm /TT2 1 Tf [ (')
0.2 (%) -0.1 (1) -0.4 ($) -0.1 (#) -0.5 (2) -0.4 (6) 0.3 (%) -0.1 (3/) -0.3
(%) -0.1 (7) -0.2 (/) -0.3 (\)) -0.5 (\)) -0.5 (/) -0.3 (8) 0.2 (%) -0.1 (&\))
-0.5 (/) -0.3 (2) -0.4 (6) 0.3 (%) -0.1 (8) 0.2 (#) -0.5 (3") -0.4 (%) -0.1
(3") -0.4 (*) 0.4 (%) -0.1 (31) -0.4 (3/) -0.3 (9) 0.4 (#) -0.5 (&\)) ] TJ
ET

If you study PDF you know that the body text is the bracketed text from above thus we can expect to extract this raw text coding.

! " # $ % # $ % &% $ &' ( ) * % + , - % . / 0 1 ' * 2 3% 4
5
'% 1 $ # 2 6 % 3/ % 7 / ) ) / 8 % &) / 2 6 % 8 # 3" % 3" * % 31 3/ 9 # &)

Compare that with the OP observation

! " # $ % # $ % &% $ &' ( ) * % + , - % . / 0 1 ' * 2 3% 4
5
' % 1 $ # 2 6 % 3/ % 7 / ) ) / 8 % &) / 2 6 % 8 # 3" % 3" * % 31 3/ 9 # &)
%

So my bad mistake, In my extraction I missed that final (%)

So what was the real problem with "different from that included in the PDF document:"

Answer

When raw page text is placed in the page it is binary encoded as numeric data. Which to our human eyes looks like the above separate ANSI letters, but they are encoded in a PDF for simplicity as single bytes. There is a secondary PDFtoText "ToUnicode" process where the extractor has to convert the short codes into conventional CALIBRI UTF-8 screen pixels.
Here is that table

24 beginbfrange
<21><21><0054>
<22><23><0068>
<24><24><0073>
<25><25><0009>
<26><26><0061>
<27><27><006d>
<28><28><0070>
<29><29><006c>
<2a><2a><0065>
<2b><2b><0050>
<2c><2c><0044>
<2d><2d><0046>
<2e><2e><0064>
<2f><2f><006f>
<30><30><0063>
<31><31><0075>
<32><32><006e>
<33><33><0074>
<34><34><0049>
<35><35><2019>
<36><36><0067>
<37><37><0066>
<38><38><0077>
<39><39><0072>
endbfrange

Most notable in the longer Unicode is, in this case, fistly one on one with more conventional ANSI codes <0000> to <00FF>. However, there is one odd boy out <35><35><2019> so we can see that is ANSI 5 and Unicode 2019 is ’ so that single 5 on its own, has been isolated as a separate entry.

Also what about that odd % on its own at the end that I missed, why might that be ? Well look up % and it is hex <25> which in a PDF is counted as a comment but in this case converts to \U+0009 very oddly that is (Character Tabulation) which is usually discarded when building a PDF. Thus usually has no physical width.

So using the ToUnicode values in a PDFtoText conversion we can expect it to be post extraction re-coded into

This is a sample PDF document I
’
m using to follow along with the tutorial

But there seem to be other issues with that source !! (remember all those % characters have no width ?)

Solution

We need to fix the file and one very simple fix is replace the tabs with spaces by change 2 bytes from <0009> to <0020>, then resave to rebuild without error. Now extraction should be improved, but do convert with an ANSI to UTF-8 extraction such as.

pdftotext -layout -enc UTF-8 sample-fixed.pdf -

Answer 4

pdfplumber is one of the better libraries to read and extract data from pdf. It also provides ways to read table data and after struggling with a lot of such libraries, pdfplumber worked best for me.

Mind you, it works best for machine-written pdf and not scanned pdf.

import pdfplumber
with pdfplumber.open(r'D:\examplepdf.pdf') as pdf:
first_page = pdf.pages[0]
print(first_page.extract_text())

Answer 5

I will introduce another library that hasn't been mentioned yet, providing you with additional options. Extracting text from PDFs can also be achieved using IronPdf.

The IronPDF library can be added via pip. Use the command below to install IronPDF using pip:

pip install ironpdf

IronPDF Python relies on .NET 6.0, as its underlying technology. Therefore, it is necessary to have the .NET 6.0 SDK installed on your machine in order to use IronPDF Python.

from ironpdf import *
 
# Load existing PDF document
pdf = PdfDocument.FromFile("content.pdf")
 
# Extract text from PDF document
all_text = pdf.ExtractAllText()
 
# Extract text from specific page in the document
page_2_text = pdf.ExtractTextFromPage(1)

In the provided code snippet, the PDF document is imported, and a method is employed to extract text from the imported PDF document. This approach enables efficient text extraction from PDF files.

Library | Code example link

Answer 6

I was looking for a simple solution to use for python 3.x and windows. There doesn't seem to be support from textract, which is unfortunate, but if you are looking for a simple solution for windows/python 3 checkout the tika package, really straight forward for reading pdfs.

Tika-Python is a Python binding to the Apache Tika™ REST services allowing Tika to be called natively in the Python community.

from tika import parser # pip install tika

raw = parser.from_file('sample.pdf')
print(raw['content'])

Note that Tika is written in Java so you will need a Java runtime installed.

Answer 7

The below code is a solution to the question in Python 3. Before running the code, make sure you have installed the pypdf library in your environment. If not installed, open the command prompt and run the following command (instead of pip you might need pip3):

pip install pypdf --upgrade

Solution Code using pypdf > 3.0.0:

import pypdf

reader = PyPDF2.PdfReader('sample.pdf')
for page in reader.pages:
    print(page.extract_text())

Answer 8

Look at this code for PyPDF2<=1.26.0:

import PyPDF2
pdf_file = open('sample.pdf', 'rb')
read_pdf = PyPDF2.PdfFileReader(pdf_file)
page = read_pdf.getPage(0)
page_content = page.extractText()
print page_content.encode('utf-8')

The output is:

!"#$%#$%&%$&'()*%+,-%./01'*23%4
5'%1$#26%3/%7/))/8%&)/26%8#3"%3"*%313/9#&)
%

Using the same code to read a pdf from 201308FCR.pdf .The output is normal.

Its documentation explains why:

def extractText(self):
    """
    Locate all text drawing commands, in the order they are provided in the
    content stream, and extract the text.  This works well for some PDF
    files, but poorly for others, depending on the generator used.  This will
    be refined in the future.  Do not rely on the order of text coming out of
    this function, as it will change if this function is made more
    sophisticated.
    :return: a unicode string object.
    """

Answer 9

Objectives: Extract text from PDF

Required Tools:

1. Poppler for windows: wrapper for pdftotext file in windows for anaanaconda: conda install -c conda-forge

2. pdftotext utility to convert PDF to text.

Steps: Install Poppler. For windows, Add “xxx/bin/” to env path pip install pdftotext

import pdftotext
 
# Load your PDF
with open("Target.pdf", "rb") as f:
    pdf = pdftotext.PDF(f)
 
# Save all text to a txt file.
with open('output.txt', 'w') as f:
    f.write("\n\n".join(pdf))

Answer 10

It includes creating a new sheet for each PDF page being set dynamically based on number of pages in the document.

import PyPDF2 as p2
import xlsxwriter

pdfFileName = "sample.pdf"
pdfFile = open(pdfFileName, 'rb')
pdfread = p2.PdfFileReader(pdfFile)
number_of_pages = pdfread.getNumPages()
workbook = xlsxwriter.Workbook('pdftoexcel.xlsx')

for page_number in range(number_of_pages):
    print(f'Sheet{page_number}')
    pageinfo = pdfread.getPage(page_number)
    rawInfo = pageinfo.extractText().split('\n')

    row = 0
    column = 0
    worksheet = workbook.add_worksheet(f'Sheet{page_number}')

    for line in rawInfo:
        worksheet.write(row, column, line)
        row += 1
workbook.close()

Answer 11

As of 2021 I would like to recommend pdfreader due to the fact that PyPDF2/3 seems to be troublesome now and tika is actually written in java and needs a jre in the background. pdfreader is pythonic, currently well maintained and has extensive documentation here.

Installation as usual: pip install pdfreader

Short example of usage:

from pdfreader import PDFDocument, SimplePDFViewer

# get raw document
fd = open(file_name, "rb")
doc = PDFDocument(fd)

# there is an iterator for pages
page_one = next(doc.pages())
all_pages = [p for p in doc.pages()]

# and even a viewer
fd = open(file_name, "rb")
viewer = SimplePDFViewer(fd)

Answer 12

You can simply do this using pytessaract and OpenCV. Refer the following code. You can get more details from this article.

import os
from PIL import Image
from pdf2image import convert_from_path
import pytesseract

filePath = ‘021-DO-YOU-WONDER-ABOUT-RAIN-SNOW-SLEET-AND-HAIL-Free-Childrens-Book-By-Monkey-Pen.pdf’
doc = convert_from_path(filePath)

path, fileName = os.path.split(filePath)
fileBaseName, fileExtension = os.path.splitext(fileName)

for page_number, page_data in enumerate(doc):
txt = pytesseract.image_to_string(page_data).encode(“utf-8”)
print(“Page # {} — {}”.format(str(page_number),txt))

Answer 13

Camelot seems a fairly powerful solution to extract tables from PDFs in Python.

At first sight it seems to achieve almost as accurate extraction as the tabula-py package suggested by CreekGeek, which is already waaaaay above any other posted solution as of today in terms of reliability, but it is supposedly much more configurable. Furthermore it has its own accuracy indicator (results.parsing_report), and great debugging features.

Both Camelot and Tabula provide the results as Pandas’ DataFrames, so it is easy to adjust tables afterwards.

pip install camelot-py

(Not to be confused with the camelot package.)

import camelot

df_list = []
results = camelot.read_pdf("file.pdf", ...)
for table in results:
    print(table.parsing_report)
    df_list.append(results[0].df)

It can also output results as CSV, JSON, HTML or Excel.

Camelot comes at the expense of a number of dependencies.

NB : Since my input is pretty complex with many different tables I ended up using both Camelot and Tabula, depending on the table, to achieve the best results.

Answer 14

Use pdfminer.six. Here is the the doc : https://pdfminersix.readthedocs.io/en/latest/index.html

To convert pdf to text :

    def pdf_to_text():
        from pdfminer.high_level import extract_text

        text = extract_text('test.pdf')
        print(text)

Answer 15

If wanting to extract text from a table, I've found tabula to be easily implemented, accurate, and fast:

to get a pandas dataframe:

import tabula

df = tabula.read_pdf('your.pdf')

df

By default, it ignores page content outside of the table. So far, I've only tested on a single-page, single-table file, but there are kwargs to accommodate multiple pages and/or multiple tables.

install via:

pip install tabula-py
# or
conda install -c conda-forge tabula-py 

In terms of straight-up text extraction see: https://stackoverflow.com/a/63190886/9249533

Answer 16

A more robust way, supposing there are multiple PDF's or just one !

import os
from PyPDF2 import PdfFileWriter, PdfFileReader
from io import BytesIO

mydir = # specify path to your directory where PDF or PDF's are

for arch in os.listdir(mydir): 
    buffer = io.BytesIO()
    archpath = os.path.join(mydir, arch)
    with open(archpath) as f:
            pdfFileObj = open(archpath, 'rb')
            pdfReader = PyPDF2.PdfFileReader(pdfFileObj)
            pdfReader.numPages
            pageObj = pdfReader.getPage(0) 
            ley = pageObj.extractText()
            file1 = open("myfile.txt","w")
            file1.writelines(ley)
            file1.close()
            

Answer 17

In 2020 the solutions above were not working for the particular pdf I was working with. Below is what did the trick. I am on Windows 10 and Python 3.8

Test pdf file: https://drive.google.com/file/d/1aUfQAlvq5hA9kz2c9CyJADiY3KpY3-Vn/view?usp=sharing

#pip install pdfminer.six
import io

from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
from pdfminer.converter import TextConverter
from pdfminer.layout import LAParams
from pdfminer.pdfpage import PDFPage


def convert_pdf_to_txt(path):
    '''Convert pdf content from a file path to text

    :path the file path
    '''
    rsrcmgr = PDFResourceManager()
    codec = 'utf-8'
    laparams = LAParams()

    with io.StringIO() as retstr:
        with TextConverter(rsrcmgr, retstr, codec=codec,
                           laparams=laparams) as device:
            with open(path, 'rb') as fp:
                interpreter = PDFPageInterpreter(rsrcmgr, device)
                password = ""
                maxpages = 0
                caching = True
                pagenos = set()

                for page in PDFPage.get_pages(fp,
                                              pagenos,
                                              maxpages=maxpages,
                                              password=password,
                                              caching=caching,
                                              check_extractable=True):
                    interpreter.process_page(page)

                return retstr.getvalue()


if __name__ == "__main__":
    print(convert_pdf_to_txt('C:\\Path\\To\\Test_PDF.pdf')) 

Answer 18

I've try many Python PDF converters, and I like to update this review. Tika is one of the best. But PyMuPDF is a good news from @ehsaneha user.

I did a code to compare them in: https://github.com/erfelipe/PDFtextExtraction I hope to help you.

Tika-Python is a Python binding to the Apache Tika™ REST services allowing Tika to be called natively in the Python community.

from tika import parser

raw = parser.from_file("///Users/Documents/Textos/Texto1.pdf")
raw = str(raw)

safe_text = raw.encode('utf-8', errors='ignore')

safe_text = str(safe_text).replace("\n", "").replace("\\", "")
print('--- safe text ---' )
print( safe_text )

Answer 19

I've got a better work around than OCR and to maintain the page alignment while extracting the text from a PDF. Should be of help:

from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
from pdfminer.converter import TextConverter
from pdfminer.layout import LAParams
from pdfminer.pdfpage import PDFPage
from io import StringIO

def convert_pdf_to_txt(path):
    rsrcmgr = PDFResourceManager()
    retstr = StringIO()
    codec = 'utf-8'
    laparams = LAParams()
    device = TextConverter(rsrcmgr, retstr, codec=codec, laparams=laparams)
    fp = open(path, 'rb')
    interpreter = PDFPageInterpreter(rsrcmgr, device)
    password = ""
    maxpages = 0
    caching = True
    pagenos=set()


    for page in PDFPage.get_pages(fp, pagenos, maxpages=maxpages, password=password,caching=caching, check_extractable=True):
        interpreter.process_page(page)


    text = retstr.getvalue()

    fp.close()
    device.close()
    retstr.close()
    return text

text= convert_pdf_to_txt('test.pdf')
print(text)

Answer 20

For extracting Text from PDF use below code

import PyPDF2
pdfFileObj = open('mypdf.pdf', 'rb')

pdfReader = PyPDF2.PdfFileReader(pdfFileObj)

print(pdfReader.numPages)

pageObj = pdfReader.getPage(0)

a = pageObj.extractText()

print(a)

Answer 21

If you try it in Anaconda on Windows, PyPDF2 might not handle some of the PDFs with non-standard structure or unicode characters. I recommend using the following code if you need to open and read a lot of pdf files - the text of all pdf files in folder with relative path .//pdfs// will be stored in list pdf_text_list.

from tika import parser
import glob

def read_pdf(filename):
    text = parser.from_file(filename)
    return(text)


all_files = glob.glob(".\\pdfs\\*.pdf")
pdf_text_list=[]
for i,file in enumerate(all_files):
    text=read_pdf(file)
    pdf_text_list.append(text['content'])

print(pdf_text_list)

Answer 22

I found a solution here PDFLayoutTextStripper

It's good because it can keep the layout of the original PDF.

It's written in Java but I have added a Gateway to support Python.

Sample code:

from py4j.java_gateway import JavaGateway

gw = JavaGateway()
result = gw.entry_point.strip('samples/bus.pdf')

# result is a dict of {
#   'success': 'true' or 'false',
#   'payload': pdf file content if 'success' is 'true'
#   'error': error message if 'success' is 'false'
# }

print result['payload']

Sample output from PDFLayoutTextStripper:

You can see more details here Stripper with Python

Answer 23

pdftotext is the best and simplest one! pdftotext also reserves the structure as well.

I tried PyPDF2, PDFMiner and a few others but none of them gave a satisfactory result.

Answer 24

Here is the simplest code for extracting text

code:

# importing required modules
import PyPDF2

# creating a pdf file object
pdfFileObj = open('filename.pdf', 'rb')

# creating a pdf reader object
pdfReader = PyPDF2.PdfFileReader(pdfFileObj)

# printing number of pages in pdf file
print(pdfReader.numPages)

# creating a page object
pageObj = pdfReader.getPage(5)

# extracting text from page
print(pageObj.extractText())

# closing the pdf file object
pdfFileObj.close()

Answer 25

PyPDF2 does work, but results may vary. I am seeing quite inconsistent findings from its result extraction.

reader=PyPDF2.pdf.PdfFileReader(self._path)
eachPageText=[]
for i in range(0,reader.getNumPages()):
    pageText=reader.getPage(i).extractText()
    print(pageText)
    eachPageText.append(pageText)

Answer 26

You can download tika-app-xxx.jar(latest) from Here.

Then put this .jar file in the same folder of your python script file.

then insert the following code in the script:

import os
import os.path

tika_dir=os.path.join(os.path.dirname(__file__),'<tika-app-xxx>.jar')

def extract_pdf(source_pdf:str,target_txt:str):
    os.system('java -jar '+tika_dir+' -t {} > {}'.format(source_pdf,target_txt))

The advantage of this method:

fewer dependency. Single .jar file is easier to manage that a python package.

multi-format support. The position source_pdf can be the directory of any kind of document. (.doc, .html, .odt, etc.)

up-to-date. tika-app.jar always release earlier than the relevant version of tika python package.

stable. It is far more stable and well-maintained (Powered by Apache) than PyPDF.

disadvantage:

A jre-headless is necessary.

Answer 27

Multi - page pdf can be extracted as text at single stretch instead of giving individual page number as argument using below code

import PyPDF2
import collections
pdf_file = open('samples.pdf', 'rb')
read_pdf = PyPDF2.PdfFileReader(pdf_file)
number_of_pages = read_pdf.getNumPages()
c = collections.Counter(range(number_of_pages))
for i in c:
   page = read_pdf.getPage(i)
   page_content = page.extractText()
   print page_content.encode('utf-8')

Answer 28

After trying textract (which seemed to have too many dependencies) and pypdf2 (which could not extract text from the pdfs I tested with) and tika (which was too slow) I ended up using pdftotext from xpdf (as already suggested in another answer) and just called the binary from python directly (you may need to adapt the path to pdftotext):

import os, subprocess
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
args = ["/usr/local/bin/pdftotext",
        '-enc',
        'UTF-8',
        "{}/my-pdf.pdf".format(SCRIPT_DIR),
        '-']
res = subprocess.run(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
output = res.stdout.decode('utf-8')

There is pdftotext which does basically the same but this assumes pdftotext in /usr/local/bin whereas I am using this in AWS lambda and wanted to use it from the current directory.

Btw: For using this on lambda you need to put the binary and the dependency to libstdc++.so into your lambda function. I personally needed to compile xpdf. As instructions for this would blow up this answer I put them on my personal blog.

Answer 29

I am adding code to accomplish this: It is working fine for me:

# This works in python 3
# required python packages
# tabula-py==1.0.0
# PyPDF2==1.26.0
# Pillow==4.0.0
# pdfminer.six==20170720

import os
import shutil
import warnings
from io import StringIO

import requests
import tabula
from PIL import Image
from PyPDF2 import PdfFileWriter, PdfFileReader
from pdfminer.converter import TextConverter
from pdfminer.layout import LAParams
from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
from pdfminer.pdfpage import PDFPage

warnings.filterwarnings("ignore")


def download_file(url):
    local_filename = url.split('/')[-1]
    local_filename = local_filename.replace("%20", "_")
    r = requests.get(url, stream=True)
    print(r)
    with open(local_filename, 'wb') as f:
        shutil.copyfileobj(r.raw, f)

    return local_filename


class PDFExtractor():
    def __init__(self, url):
        self.url = url

    # Downloading File in local
    def break_pdf(self, filename, start_page=-1, end_page=-1):
        pdf_reader = PdfFileReader(open(filename, "rb"))
        # Reading each pdf one by one
        total_pages = pdf_reader.numPages
        if start_page == -1:
            start_page = 0
        elif start_page < 1 or start_page > total_pages:
            return "Start Page Selection Is Wrong"
        else:
            start_page = start_page - 1

        if end_page == -1:
            end_page = total_pages
        elif end_page < 1 or end_page > total_pages - 1:
            return "End Page Selection Is Wrong"
        else:
            end_page = end_page

        for i in range(start_page, end_page):
            output = PdfFileWriter()
            output.addPage(pdf_reader.getPage(i))
            with open(str(i + 1) + "_" + filename, "wb") as outputStream:
                output.write(outputStream)

    def extract_text_algo_1(self, file):
        pdf_reader = PdfFileReader(open(file, 'rb'))
        # creating a page object
        pageObj = pdf_reader.getPage(0)

        # extracting extract_text from page
        text = pageObj.extractText()
        text = text.replace("\n", "").replace("\t", "")
        return text

    def extract_text_algo_2(self, file):
        pdfResourceManager = PDFResourceManager()
        retstr = StringIO()
        la_params = LAParams()
        device = TextConverter(pdfResourceManager, retstr, codec='utf-8', laparams=la_params)
        fp = open(file, 'rb')
        interpreter = PDFPageInterpreter(pdfResourceManager, device)
        password = ""
        max_pages = 0
        caching = True
        page_num = set()

        for page in PDFPage.get_pages(fp, page_num, maxpages=max_pages, password=password, caching=caching,
                                      check_extractable=True):
            interpreter.process_page(page)

        text = retstr.getvalue()
        text = text.replace("\t", "").replace("\n", "")

        fp.close()
        device.close()
        retstr.close()
        return text

    def extract_text(self, file):
        text1 = self.extract_text_algo_1(file)
        text2 = self.extract_text_algo_2(file)

        if len(text2) > len(str(text1)):
            return text2
        else:
            return text1

    def extarct_table(self, file):

        # Read pdf into DataFrame
        try:
            df = tabula.read_pdf(file, output_format="csv")
        except:
            print("Error Reading Table")
            return

        print("\nPrinting Table Content: \n", df)
        print("\nDone Printing Table Content\n")

    def tiff_header_for_CCITT(self, width, height, img_size, CCITT_group=4):
        tiff_header_struct = '<' + '2s' + 'h' + 'l' + 'h' + 'hhll' * 8 + 'h'
        return struct.pack(tiff_header_struct,
                           b'II',  # Byte order indication: Little indian
                           42,  # Version number (always 42)
                           8,  # Offset to first IFD
                           8,  # Number of tags in IFD
                           256, 4, 1, width,  # ImageWidth, LONG, 1, width
                           257, 4, 1, height,  # ImageLength, LONG, 1, lenght
                           258, 3, 1, 1,  # BitsPerSample, SHORT, 1, 1
                           259, 3, 1, CCITT_group,  # Compression, SHORT, 1, 4 = CCITT Group 4 fax encoding
                           262, 3, 1, 0,  # Threshholding, SHORT, 1, 0 = WhiteIsZero
                           273, 4, 1, struct.calcsize(tiff_header_struct),  # StripOffsets, LONG, 1, len of header
                           278, 4, 1, height,  # RowsPerStrip, LONG, 1, lenght
                           279, 4, 1, img_size,  # StripByteCounts, LONG, 1, size of extract_image
                           0  # last IFD
                           )

    def extract_image(self, filename):
        number = 1
        pdf_reader = PdfFileReader(open(filename, 'rb'))

        for i in range(0, pdf_reader.numPages):

            page = pdf_reader.getPage(i)

            try:
                xObject = page['/Resources']['/XObject'].getObject()
            except:
                print("No XObject Found")
                return

            for obj in xObject:

                try:

                    if xObject[obj]['/Subtype'] == '/Image':
                        size = (xObject[obj]['/Width'], xObject[obj]['/Height'])
                        data = xObject[obj]._data
                        if xObject[obj]['/ColorSpace'] == '/DeviceRGB':
                            mode = "RGB"
                        else:
                            mode = "P"

                        image_name = filename.split(".")[0] + str(number)

                        print(xObject[obj]['/Filter'])

                        if xObject[obj]['/Filter'] == '/FlateDecode':
                            data = xObject[obj].getData()
                            img = Image.frombytes(mode, size, data)
                            img.save(image_name + "_Flate.png")
                            # save_to_s3(imagename + "_Flate.png")
                            print("Image_Saved")

                            number += 1
                        elif xObject[obj]['/Filter'] == '/DCTDecode':
                            img = open(image_name + "_DCT.jpg", "wb")
                            img.write(data)
                            # save_to_s3(imagename + "_DCT.jpg")
                            img.close()
                            number += 1
                        elif xObject[obj]['/Filter'] == '/JPXDecode':
                            img = open(image_name + "_JPX.jp2", "wb")
                            img.write(data)
                            # save_to_s3(imagename + "_JPX.jp2")
                            img.close()
                            number += 1
                        elif xObject[obj]['/Filter'] == '/CCITTFaxDecode':
                            if xObject[obj]['/DecodeParms']['/K'] == -1:
                                CCITT_group = 4
                            else:
                                CCITT_group = 3
                            width = xObject[obj]['/Width']
                            height = xObject[obj]['/Height']
                            data = xObject[obj]._data  # sorry, getData() does not work for CCITTFaxDecode
                            img_size = len(data)
                            tiff_header = self.tiff_header_for_CCITT(width, height, img_size, CCITT_group)
                            img_name = image_name + '_CCITT.tiff'
                            with open(img_name, 'wb') as img_file:
                                img_file.write(tiff_header + data)

                            # save_to_s3(img_name)
                            number += 1
                except:
                    continue

        return number

    def read_pages(self, start_page=-1, end_page=-1):

        # Downloading file locally
        downloaded_file = download_file(self.url)
        print(downloaded_file)

        # breaking PDF into number of pages in diff pdf files
        self.break_pdf(downloaded_file, start_page, end_page)

        # creating a pdf reader object
        pdf_reader = PdfFileReader(open(downloaded_file, 'rb'))

        # Reading each pdf one by one
        total_pages = pdf_reader.numPages

        if start_page == -1:
            start_page = 0
        elif start_page < 1 or start_page > total_pages:
            return "Start Page Selection Is Wrong"
        else:
            start_page = start_page - 1

        if end_page == -1:
            end_page = total_pages
        elif end_page < 1 or end_page > total_pages - 1:
            return "End Page Selection Is Wrong"
        else:
            end_page = end_page

        for i in range(start_page, end_page):
            # creating a page based filename
            file = str(i + 1) + "_" + downloaded_file

            print("\nStarting to Read Page: ", i + 1, "\n -----------===-------------")

            file_text = self.extract_text(file)
            print(file_text)
            self.extract_image(file)

            self.extarct_table(file)
            os.remove(file)
            print("Stopped Reading Page: ", i + 1, "\n -----------===-------------")

        os.remove(downloaded_file)


# I have tested on these 3 pdf files
# url = "http://s3.amazonaws.com/NLP_Project/Original_Documents/Healthcare-January-2017.pdf"
url = "http://s3.amazonaws.com/NLP_Project/Original_Documents/Sample_Test.pdf"
# url = "http://s3.amazonaws.com/NLP_Project/Original_Documents/Sazerac_FS_2017_06_30%20Annual.pdf"
# creating the instance of class
pdf_extractor = PDFExtractor(url)

# Getting desired data out
pdf_extractor.read_pages(15, 23)

Answer 30

Use textract.

• http://textract.readthedocs.io/en/latest/
• https://github.com/deanmalmgren/textract

It supports many types of files including PDFs

import textract
text = textract.process("path/to/file.extension")