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Hey there all, today, in this post,I will explain you a proper way to extract text from a pdf. Installation For extraction , we need a python module named ‘pdf2image’. We can install this module by using this command in your terminal. pip install pdf2image Import the Module First, we will import all the packages. You need pdf2image to convert PDF files to ppm image files. We will also manipulate the paths to join and rename text files, so that we can import the os and sys packages. The following part calls a PIL library and imports the image with pytesseract: import pdf2image import os, sys try: from PIL import Image except ImportError: import Image import pytesseract In the next step we have initialize the path of your documents and the counter to be used later. PATH = 'Enter your path' #initialize the counter that you will use later in your pdf extraction function i = 1 Some unrequired files will also be created with this function. So in this step , we will delete these files which are not required. def delete_ppms(): for file in os.listdir(PATH): if '.ppm' in file or '.DS_Store' in file: try: os.remove(PATH + file) except FileNotFoundError: pass Sorting Files Now will sort the pdf files according to their types. pdf_files = [] docx_files = [] for f in os.listdir(PATH): full_name = os.path.join(PATH, f) if os.path.isfile(full_name): name = os.path.basename(f) filename, ext =os.path.splitext(name) if ext == '.pdf': pdf_files.append(name) elif ext == ('.docx'): docx_files.append(name) After this piece of code, we can extract text from pdf using pdf_extract function. Last Step This print function will help you see which file is currently checked out: def pdf_extract(file, i): print("extracting from file:", file) delete_ppms() images = pdf2image.convert_from_path(PATH + file, output_folder=PATH) j = 0 for file in sorted (os.listdir(PATH)): if '.ppm' in file and 'image' not in file: os.rename(PATH + file, PATH + 'image' + str(i) + '-' + str(j) + '.ppm') j += 1 j = 0 f = open(PATH +'result{}.txt'.format(i), 'w') files = [f for f in os.listdir(PATH) if '.ppm' in f] for file in sorted(files, key=lambda x: int(x[x.index('-') + 1: x.index('.')])): temp = pytesseract.image_to_string(Image.open(PATH + file)) f.write(temp) f.close() The last step in this project , when you will run the below command,you will go to the directory you will see a text file by the name of result1.txt with all the text extracted from the PDF file. Code : for i in range(len(pdf_files)): pdf_file = pdf_files[i] pdf_extract(pdf_file, i) Thats all for today . Hope this is useful 😇

⚡️ ​10 Ways to Speed Up Your Python Code ⚡️ 1. List Comprehensions numbers = [x**2 for x in range(100000) if x % 2 == 0] instead of = [] for x in range(100000): if x % 2 == 0: numbers.append(x**2) 2. Use the Built-In Functions Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution. 3. Function Calls Are Expensive Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration. 4. Lazy Module Importing If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything. 5. Take Advantage of Numpy Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter. 6. Try Multiprocessing Multiprocessing can bring large performance increases to a Python script, but it can be difficult to implement properly compared to other methods mentioned in this post. 7. Be Careful with Bulky Libraries One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code. 8. Avoid Global Variables Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible. 9. Try Multiple Solutions Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure. 10. Think About Your Data Structures Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets. 🔗Source ━━━━━━━━━━━━━ Share nd support 🤟😉

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