Extracting text from PDF for NLP tasks

Introduction

Natural Language Processing is a task that involves data collection from various sources and not every time one is lucky to get the baked data. Many times you have to extract data from various sources, one of them is Files.

In this post, I will be talking specifically about the PDF files.

Getting the Guns ready

After some exploration on the internet, I came across a python package PyPDF which sounded a good contender to achieve what we want (text extraction), although it can do more than what we need. This package can also be used to generate, decrypting and merging PDF files, although its usage details are not that clear that's why I thought of writing a post to explain it.

Installation

pip install PyPDF2

Reading the File and extracting Text

import PyPDF2 
filename = 'complete path of your pdf file' 

#opening the file 
pdfFileObj = open(filename,'rb')

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

#Discerning the number of pages will allow us to parse through all the pages.
num_pages = pdfReader.numPages

count = 0
text = ""


#The while loop will read each page.
while count < num_pages:
    
    pageObj = pdfReader.getPage(count)
    count +=1
    text += pageObj.extractText()

print(text)

Using the above code one can easily extract text from the PDF files.

Also, have a look at some posts related to NLP

GROKs for Information Extraction
Using GROKs in Python
Document Vectorization
GloVE Vectorization
Word Vectorization

To read more about the text extraction and other tech-related stuff do have a look at my blog.