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

I’m using the PyPDF2 module, and have the following script:

import PyPDF2
pdf_file = open('sample.pdf')
read_pdf = PyPDF2.PdfFileReader(pdf_file)
number_of_pages = read_pdf.getNumPages()
page = read_pdf.getPage(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:

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

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

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

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")

Look at this code:

import PyPDF2
pdf_file = open('sample.pdf', 'rb')
read_pdf = PyPDF2.PdfFileReader(pdf_file)
number_of_pages = read_pdf.getNumPages()
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.
    """

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.

You may want to use time proved xPDF and derived tools to extract text instead as pyPDF2 seems to have various issues with the text extraction still.

The long answer is that there are lot of variations how a text is encoded inside PDF and that it may require to decoded PDF string itself, then may need to map with CMAP, then may need to analyze distance between words and letters etc.

In case the PDF is damaged (i.e. displaying the correct text but when copying it gives garbage) and you really need to extract text, then you may want to consider converting PDF into image (using ImageMagik) and then use Tesseract to get text from image using OCR.

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 )

The below code is a solution to the question in Python 3. Before running the code, make sure you have installed the PyPDF2 library in your environment. If not installed, open the command prompt and run the following command:

pip3 install PyPDF2

Solution Code:

import PyPDF2
pdfFileObject = open('sample.pdf', 'rb')
pdfReader = PyPDF2.PdfFileReader(pdfFileObject)
count = pdfReader.numPages
for i in range(count):
    page = pdfReader.getPage(i)
    print(page.extractText())

PyPDF2 in some cases ignores the white spaces and makes the result text a mess, but I use PyMuPDF and I’m really satisfied you can use this link for more info

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')

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.

You can use PDFtoText https://github.com/jalan/pdftotext

PDF to text keeps text format indentation, doesn’t matter if you have tables.

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()

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

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)

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)

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)

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.

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)

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)