Python: Baca PDF bahasa Inggris untuk jadi text file
Revision as of 19:34, 28 October 2018 by Onnowpurbo (talk | contribs) (Created page with "Sumber: https://medium.com/@rqaiserr/how-to-convert-pdfs-into-searchable-key-words-with-python-85aab86c544f Persiapan pip install PyPDF2 pip install textract pip install...")
Sumber: https://medium.com/@rqaiserr/how-to-convert-pdfs-into-searchable-key-words-with-python-85aab86c544f
Persiapan
pip install PyPDF2 pip install textract pip install nltk
Code
# Load Library import PyPDF2 import textract from nltk.tokenize import word_tokenize from nltk.corpus import stopwords
#write a for-loop to open many files filename = 'enter the name of the file here' #open allows you to read the file pdfFileObj = open(filename,'rb') #The pdfReader variable is a readable object that will be parsed 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()
#This if statement exists to check if the above library returned #words. It's done because PyPDF2 cannot read scanned files.
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
else:
text = textract.process(fileurl, method='tesseract', language='eng')
# Now we have a text variable which contains all the text derived #from our PDF file. Type print(text) to see what it contains. It #likely contains a lot of spaces, possibly junk such as '\n' etc.
# Now, we will clean our text variable, and return it as a list of keywords.
#The word_tokenize() function will break our text phrases into #individual words
tokens = word_tokenize(text)
#we'll create a new list which contains punctuation we wish to clean
punctuations = ['(',')',';',':','[',']',',']
#We initialize the stopwords variable which is a list of words like #"The", "I", "and", etc. that don't hold much value as keywords
stop_words = stopwords.words('english')
#We create a list comprehension which only returns a list of words #that are NOT IN stop_words and NOT IN punctuations.
keywords = [word for word in tokens if not word in stop_words and not word in punctuations]