In my experience, PyMuPDF is the best open-source Python library for this, better than PDFplumber, PyPDF2, and others. For paid options, Azure Document Intelligence is excellent; it can even handle unstructured tables. Answer from ImGallo on reddit.com
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GitHub
github.com › google › langextract
GitHub - google/langextract: A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization. · GitHub
import langextract as lx import textwrap # 1. Define the prompt and extraction rules prompt = textwrap.dedent("""\ Extract characters, emotions, and relationships in order of appearance. Use exact text for extractions. Do not paraphrase or overlap entities. Provide meaningful attributes for each entity to add context.""") # 2.
Author   google
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Computer Hope
computerhope.com › issues › ch001721.htm
How to Extract Specific Portions of a Text File Using Python
June 1, 2025 - In this guide, we'll discuss some simple ways to extract text from a file using the Python 3 programming language. Make sure you're using Python 3. Reading data from a text file. ... Reading text files line-by-line. Storing text data in a variable. Searching text for a substring. Incorporating regular expressions. Putting it all together. Related information. In this guide, we'll be using Python version 3. Most systems come pre-installed with Python 2.7. While Python 2.7 is used in legacy code, Python 3 is the present and future of the Python language.
Discussions

What’s the Best Python Library for Extracting Text from PDFs?
In my experience, PyMuPDF is the best open-source Python library for this, better than PDFplumber, PyPDF2, and others. For paid options, Azure Document Intelligence is excellent; it can even handle unstructured tables. More on reddit.com
🌐 r/LangChain
88
85
July 19, 2024
python - Extract data from lines of a text file - Stack Overflow
Find centralized, trusted content ... you use most. Learn more about Collectives ... Bring the best of human thought and AI automation together at your work. Explore Stack Internal ... I need to extract data from lines of a text file. The data is name and scoring information formatted like this: Shyvana - 12/4/5 - Loss - 2012-11-22 Fizz - 12/4/5 - Win - 2012-11-22 Miss Fortune - 12/4/3 - Win - 2012-11-22 · This file is generated by another part of my little python program where ... More on stackoverflow.com
🌐 stackoverflow.com
Introducing Kreuzberg: A Simple, Modern Library for PDF and Document Text Extraction in Python
Sounds very similar to docling , which is fairly mature and well integrated. Did you find any shortcomings of docling that are solved with this library? More on reddit.com
🌐 r/Python
83
335
February 1, 2025
what's the best route to extract text from this image with python and opencv?
Tesseract typically expects a page of text, such as what you'd get from scanning in a book on a flatbed scanner. It doesn't work well with text from a "real world" image, nor when the text is skewed ( https://www.ccoderun.ca/programming/2022-03-26_tesseract_image_rotation/ ), and especially not when the text is not a dictionary word. First thing you should do is turn off dictionary word verification. Look at the settings such as load_system_dawg and load_freq_dawg. Also look at the PSM settings for Tesseract. You may want to consider using object detection instead of OCR. You could then easily identify the frames, and probably read the values for each ball in the frames. For example, see what I do with Darknet/YOLO and text, such as this video: https://www.youtube.com/watch?v=XxhbXccHEpA More on reddit.com
🌐 r/computervision
6
6
November 19, 2023
People also ask

Can I parse PDFs in Python without using OCR?

Yes! If your PDF contains digital (selectable) text, you can extract it using PyPDF without OCR. This works best for PDFs exported from Word, LaTeX, or similar tools.

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nutrient.io
nutrient.io › blog › sdk › extract text from pdf using python
Parse PDFs with Python: Step-by-step text extraction tutorial
Can I extract structured data like paragraphs or table-like sections?

Absolutely. The Nutrient Processor API returns both plain text and structured JSON with text order and hierarchy, making it ideal for NLP or analysis pipelines.

🌐
nutrient.io
nutrient.io › blog › sdk › extract text from pdf using python
Parse PDFs with Python: Step-by-step text extraction tutorial
Is PyPDF enough for enterprise projects?

Not always. PyPDF is great for simple tasks, but for large-scale, secure, or OCR-heavy workflows, a robust API like Nutrient’s is better suited.

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nutrient.io
nutrient.io › blog › sdk › extract text from pdf using python
Parse PDFs with Python: Step-by-step text extraction tutorial
🌐
Medium
medium.com › @gunkurnia › mastering-text-extraction-in-python-a-comprehensive-guide-aa4450254f0e
Mastering Text Extraction in Python: A Comprehensive Guide | by GunKurnia | Medium
December 1, 2024 - text = "Hello, welcome to Python programming!" # Extract text using index positions extracted_text = text[18:24] print(extracted_text) # Output: Python
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Nutrient
nutrient.io › blog › sdk › extract text from pdf using python
Parse PDFs with Python: Step-by-step text extraction tutorial
3 weeks ago - Learn to extract text from PDFs in Python using PyPDF for quick jobs and the Nutrient Processor API for OCR, structured output, and secure document parsing. Ideal for automation workflows and large-scale processing.
🌐
Xbyte
xbyte.io › home › comprehensive guide to text data extraction using python
Comprehensive Guide to Text Data Extraction Using Python
October 13, 2025 - Step-by-step guide to text data extraction in Python. Learn methods to clean, process, and analyze unstructured data effectively.
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Reddit
reddit.com › r/langchain › what’s the best python library for extracting text from pdfs?
r/LangChain on Reddit: What’s the Best Python Library for Extracting Text from PDFs?
July 19, 2024 -

Hello everyone, I hope you're all doing well! I’m currently on the lookout for a library that can extract text in paragraph chunks from PDFs. For instance, I need it to pull out the Introduction with all its paragraphs separately, the Conclusion with all its paragraphs separately, and so on, essentially chunking the text by paragraphs. Do you have any suggestions? Thanks!

Find elsewhere
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Analytics Vidhya
analyticsvidhya.com › home › hands-on nlp project: a comprehensive guide to information extraction using python
Hands-on NLP Project: A Comprehensive Guide to Information Extraction using Python
October 15, 2024 - For example, take the word “right”. In the sentence, “The boy was awarded chocolate for giving the right answer”, “right” is used as an adjective. Whereas, in the sentence, “You have the right to say whatever you want”, “right” is treated as a noun. This goes to show that the POS tag of a word carries a lot of significance when it comes to understanding the meaning of a sentence. And we can leverage it to extract meaningful information from our text. Let’s take an example to understand this. We’ll be using the popular spaCy library here. Python Code: import spacy # load english language model nlp = spacy.load('en_core_web_sm',disable=['ner','textcat']) text = "This is a sample sentence."
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Towards Data Science
towardsdatascience.com › home › latest › 3 python modules you should know to extract text data
3 Python Modules You Should Know to Extract Text Data | Towards Data Science
January 29, 2025 - If we want to extract text or tabular data from any document, this library can be much handy. To install this library, open the command prompt and type the below command. Make sure that the python is available in the machine. ... To use this library, first, we need to import it and then use pdfplumber.open to read any pdf files.
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Cloudinary
cloudinary.com › home › extract text from images in python with pillow and pytesseract
Extract Text from Images in Python with Pillow and pytesseract | Cloudinary
November 23, 2025 - A Python wrapper at its core, Pytesseract simplifies extracting text from images, offering developers a user-friendly interface to leverage Tesseract’s capabilities. With just a few lines of code, you can convert images—ranging from scanned documents to photos of text in the wild—into manipulable strings of data.
🌐
Medium
onlyoneaman.medium.com › i-tested-7-python-pdf-extractors-so-you-dont-have-to-2025-edition-c88013922257
I Tested 7 Python PDF Extractors So You Don’t Have To (2025 Edition) | by Aman Kumar | Medium
July 21, 2025 - Testing Approach: I used the simplest possible implementation for each library — the approach you’d try first when you’re just getting started. Most of these packages have advanced configuration options, specialised table extraction methods, and layout analysis features that could dramatically change results. But I wanted to see what you get with minimal effort. ... # pip install pypdfium2 import pypdfium2 as pdfium text = "\n".join( p.get_textpage().get_text_range() for p in pdfium.PdfDocument("doc.pdf") )
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GeeksforGeeks
geeksforgeeks.org › python › python-extract-words-from-given-string
Python | Extract words from given string - GeeksforGeeks
July 11, 2025 - Explanation: split() is used to extract each word from Str and it seperates the words based on spaces and return them as a list. Regular expressions allow extracting words from text with punctuation or special characters. Python’s re.findall() helps filter out only the valid words.
🌐
Medium
medium.com › data-science › top-5-python-libraries-for-extracting-text-from-images-c29863b2f3d
Top 5 Python OCR Libraries for Extracting Text from Images | TDS Archive
October 21, 2024 - Discover the top 5 Python OCR libraries, including pytesseract, EasyOCR, and docTR, to easily extract text from images. Learn how to master OCR with Python for your next project.
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ScrapingBee
scrapingbee.com › blog › parsel-python
Python extract text from HTML: Library guide for developers | ScrapingBee
January 11, 2026 - To extract text from HTML in Python, use BeautifulSoup and its get_text() method. Parse the HTML, remove scripts and styles, then extract the readable content.
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Braintrust
braintrust.dev › recipes › using python functions to extract text from images
Using Python functions to extract text from images - Braintrust
5 days ago - In just a few lines of code, it takes an image URL, parses and extracts the text, and returns the text contained in the image.
Top answer
1 of 5
12

The following will read everything into a dictionary keyed by player name. The value associated with each player is itself a dictionary acting as a record with named fields associated with the items converted to a format suitable for further processing.

info = {}
with open('scoring_info.txt') as input_file:
    for line in input_file:
        player, stats, outcome, date = (
            item.strip() for item in line.split('-', 3))
        stats = dict(zip(('kills', 'deaths', 'assists'),
                          map(int, stats.split('/'))))
        date = tuple(map(int, date.split('-')))
        info[player] = dict(zip(('stats', 'outcome', 'date'),
                                (stats, outcome, date)))

print('info:')
for player, record in info.items():
    print('  player %r:' % player)
    for field, value in record.items():
        print('    %s: %s' % (field, value))

# sample usage
player = 'Fizz'
print('\n%s had %s kills in the game' % (player, info[player]['stats']['kills']))

Output:

info:
  player 'Shyvana':
    date: (2012, 11, 22)
    outcome: Loss
    stats: {'assists': 5, 'kills': 12, 'deaths': 4}
  player 'Miss Fortune':
    date: (2012, 11, 22)
    outcome: Win
    stats: {'assists': 3, 'kills': 12, 'deaths': 4}
  player 'Fizz':
    date: (2012, 11, 22)
    outcome: Win
    stats: {'assists': 5, 'kills': 12, 'deaths': 4}

Fizz had 12 kills in the game

Alternatively, rather than holding most of the data in dictionaries, which can make nested-field access a little awkward — info[player]['stats']['kills'] — you could instead use a little more advanced "generic" class to hold them, which will let you write info2[player].stats.kills instead.

To illustrate, here's almost the same thing using a class I've named Struct because it's somewhat like the C language's struct data type:

class Struct(object):
    """ Generic container object """
    def __init__(self, **kwds): # keyword args define attribute names and values
        self.__dict__.update(**kwds)

info2 = {}
with open('scoring_info.txt') as input_file:
    for line in input_file:
        player, stats, outcome, date = (
            item.strip() for item in line.split('-', 3))
        stats = dict(zip(('kills', 'deaths', 'assists'),
                          map(int, stats.split('/'))))
        victory = (outcome.lower() == 'win') # change to boolean T/F
        date = dict(zip(('year','month','day'), map(int, date.split('-'))))
        info2[player] = Struct(champ_name=player, stats=Struct(**stats),
                               victory=victory, date=Struct(**date))
print('info2:')
for rec in info2.values():
    print('  player %r:' % rec.champ_name)
    print('    stats: kills=%s, deaths=%s, assists=%s' % (
          rec.stats.kills, rec.stats.deaths, rec.stats.assists))
    print('    victorious: %s' % rec.victory)
    print('    date: %d-%02d-%02d' % (rec.date.year, rec.date.month, rec.date.day))

# sample usage
player = 'Fizz'
print('\n%s had %s kills in the game' % (player, info2[player].stats.kills))

Output:

info2:
  player 'Shyvana':
    stats: kills=12, deaths=4, assists=5
    victorious: False
    date: 2012-11-22
  player 'Miss Fortune':
    stats: kills=12, deaths=4, assists=3
    victorious: True
    date: 2012-11-22
  player 'Fizz':
    stats: kills=12, deaths=4, assists=5
    victorious: True
    date: 2012-11-22

Fizz had 12 kills in the game
2 of 5
3

You want to use split (' - ') to get the parts, then perhaps again to get the numbers:

for line in yourfile.readlines ():
    data = line.split (' - ')
    nums = [int (x) for x in data[1].split ('/')]

Should get you all the stuff you need in data[] and nums[]. Alternatively, you can use the re module and write a regular expression for it. This doesn't seem complex enough for that, though.

🌐
GeeksforGeeks
geeksforgeeks.org › python › text-detection-and-extraction-using-opencv-and-ocr
Text Detection and Extraction using OpenCV and OCR - GeeksforGeeks
In this article, we explore how ... required libraries using following commands: ... Import the required Python libraries like OpenCV, pytesseract and matplotlib....
Published   July 12, 2025
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Analytics Vidhya
analyticsvidhya.com › home › top 8 ocr libraries in python to extract text from image
Top 8 OCR Libraries in Python to Extract Text from Image
April 16, 2025 - A Python wrapper called Pytesseract allows Tesseract-OCR Engine from Google to be integrated into Python programs. It offers an efficient method for optical character recognition. Because of its intuitive interface, users may extract text from photographs with little to no coding knowledge.
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Readthedocs
textract.readthedocs.io › en › stable
textract — textract 2.0.0 documentation
Of course, textract isn’t the first project with the aim to provide a simple interface for extracting text from any document. But this is, to the best of my knowledge, the only project that is written in python (a language commonly chosen by the natural language processing community) and ...
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GitHub
github.com › btimby › fulltext
GitHub - btimby/fulltext: Python library for extracting text from various file formats (for indexing). · GitHub
Python library for extracting text from various file formats (for indexing). - btimby/fulltext
Author   btimby
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SSOJet
ssojet.com › blog › python-ocr-text-extraction-guide
Text Detection and Extraction From Images Using OCR in Python | SSOJet - Enterprise SSO & Identity Solutions
December 4, 2025 - Learn how to detect and extract text from images and scanned files using Python and OCR. Step-by-step guide for developers and automation enthusiasts.