Converting Pdf Into Word

As we see, the pages of the PDF were converted to images. Then the images were read, and the content was written into a text file. Advantages of this method include: Avoiding text-based conversion because of encoding scheme resulting in loss of data. Even handwritten content in PDF can be recognized due to the usage of OCR. Recognizing only particular pages of the PDF is also possible. Getting the text as a variable so that any amount of required pre-processing can be done. Disadvantages of this method include: Disk storage is used to store the images in the local system. Although these images are tiny in size. Using OCR cannot guarantee 100% accuracy. Given a computer typed PDF document results in very high accuracy. Handwritten PDFs are still recognized, but the accuracy depends on various factors like handwriting, page color, etc. Part #1 deals with converting the PDF into image files. Each page of the PDF is stored as an image file. The names of the images stored are: PDF page 1 -> [login to view URL] PDF page 2 -> [login to view URL] PDF page 3 -> [login to view URL] …. PDF page n -> [login to view URL] Part #2 deals with recognizing text from the image files and storing it into a text file. Here, we process the images and convert it into text. Once we have the text as a string variable, we can do any processing on the text. For example, in many PDFs, when a line is completed, but a particular word cannot be written entirely in the same line, a hyphen (‘-‘) is added, and the word is continued on the next line. For example – This is some sample text but this parti- cular word could not be written in the same line. Now for such words, a fundamental pre-processing is done to convert the hyphen and the new line into a full word. After all the pre-processing is done, this text is stored in a separate text file. To get the input PDF files used in the code, click [login to view URL] Below is the implementation: filter_none brightness_4 # Import libraries from PIL import Image import pytesseract import sys from pdf2image import convert_from_path import os # Path of the pdf PDF_file = "[login to view URL]" ''' Part #1 : Converting PDF to images ''' # Store all the pages of the PDF in a variable pages = convert_from_path(PDF_file, 500) # Counter to store images of each page of PDF to image image_counter = 1 # Iterate through all the pages stored above for page in pages: # Declaring filename for each page of PDF as JPG # For each page, filename will be: # PDF page 1 -> [login to view URL] # PDF page 2 -> [login to view URL] # PDF page 3 -> [login to view URL] # .... # PDF page n -> [login to view URL] filename = "page_"+str(image_counter)+".jpg" # Save the image of the page in system [login to view URL](filename, 'JPEG') # Increment the counter to update filename image_counter = image_counter + 1 ''' Part #2 - Recognizing text from the images using OCR ''' 3 # Variable to get count of total number of pages filelimit = image_counter-1 # Creating a text file to write the output outfile = "[login to view URL]" # Open the file in append mode so that # All contents of all images are added to the same file f = open(outfile, "a") # Iterate from 1 to total number of pages for i in range(1, filelimit + 1): # Set filename to recognize text from # Again, these files will be: # [login to view URL] # [login to view URL] # .... # [login to view URL] filename = "page_"+str(i)+".jpg" # Recognize the text as string in image using pytesserct text = str(((pytesseract.image_to_string([login to view URL](filename))))) # The recognized text is stored in variable text # Any string processing may be applied on text # Here, basic formatting has been done: # In many PDFs, at line ending, if a word can't # be written fully, a 'hyphen' is added. # The rest of the word is written in the next line # Eg: This is a sample text this word here GeeksF- # orGeeks is half on .