![]() ![]() We compared 4 open-source methods in python for text extraction from pdfs with these guidelines in mind. ![]() Since we anticipate needing to process thousands of pdfs, it’s also important that our process be time-friendly. Since SIAi’s text data will be used for NLP, sentiment analysis, and further data exploration, it is critical that the extracted text be as accurate as possible. Pdfs can vary from being scanned copies of old documents to being computer-generated articles, which affects how well a program can “read” the text within a pdf. Many articles and primary sources of information are stored as pdfs.ĭespite Portable Document Format, or pdf, being one of the most common formats for document storage, it is not standardized. All code provided at github link at the end of the article.Īt Social Impact Analytics Institute, we are working to gather information and find patterns in the collected data about social issues. You can also use it to create a recommender system for resumes for jobs.In comparing 4 python packages for pdf text extraction, PyMuPdf was found to be an optimum choice due to its low Levenshtein distance, high cosine and tf-idf similarity, and fast processing time, though all 4 packages performed very well in general and Grobid produced the cleanest text output. Store it in a spreadsheet if you want to make the PDF searchable or parse a lot of files and conduct a cluster analysis. Now you have keywords for your file stored as a list. stop_words = stopwords.words('english') #We create a list comprehension that only returns a list of words that are NOT IN stop_words and NOT IN punctuations. punctuations = ',','] #We initialize the stopwords variable, which is a list of words like "The," "I," "and," etc. tokens = word_tokenize(text) #We'll create a new list that contains punctuation we wish to clean. Step 3: Convert text into keywords #The word_tokenize() function will break our text phrases into individual words. #Now, we will clean our text variable and return it as a list of keywords. It likely contains a lot of spaces, possibly junk such as '\n,' etc. Type print(text) to see what it contains. ![]() else: text = textract.process(fileurl, method='tesseract', language='eng') #Now we have a text variable that contains all the text derived from our PDF file. if text != "": text = text #If the above returns as False, we run the OCR library textract to #convert scanned/image based PDF files into text. It's done because PyPDF2 cannot read scanned files. while count < num_pages: pageObj = pdfReader.getPage(count) count =1 text = pageObj.extractText() #This if statement exists to check if the above library returned words. num_pages = pdfReader.numPages count = 0 text = "" #The while loop will read each page. pdfReader = PyPDF2.PdfFileReader(pdfFileObj) #Discerning the number of pages will allow us to parse through all the pages. pdfFileObj = open(filename,'rb') #The pdfReader variable is a readable object that will be parsed. filename = ' enter the name of the file here' #open allows you to read the file. Step 1: Import all libraries import PyPDF2 import textract from nltk.tokenize import word_tokenize from rpus import stopwords Step 2: Read PDF file #Write a for-loop to open many files (leave a comment if you'd like to learn how). Start up your favorite editor and type: Note: All lines starting with # are comments. In order to do this, make sure your PDF file is stored within the folder where you’re writing your script. This will download the libraries you require to parse PDF documents and extract keywords. ![]() NLTK (to clean and convert phrases into keywords)Įach of these libraries can be installed with the following commands inside terminal (on macOS): pip install PyPDF2 pip install textract pip install nltk.textract (to convert non-trivial, scanned PDF files into text readable by Python).PyPDF2 (to convert simple, text-based PDF files into text readable by Python).You will require the following Python libraries in order to follow this tutorial: You can use any version you like (as long as it supports the relevant libraries). For this tutorial, I’ll be using Python 3.6.3. ![]()
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