The document provides a comprehensive guide on text data extraction using Python, detailing the necessary libraries like OpenCV, Pytesseract, Pillow, and Textract. It outlines a step-by-step procedure for downloading, installing, and implementing these libraries to extract text from images and other sources. Additionally, it emphasizes the importance of careful coding to avoid errors and suggests advanced tools for automatic extraction if needed.

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Comprehensive Guide to Text Data
Extraction Using Python
Text extraction is a process of extracting data or information from different sources
such as images, scanned documents, invoices, bank statements, etc. This can be a
routine task for business professionals, and sometimes for common individuals as
well.
Although, there are numerous techniques and methods available that are being
leveraged for text extraction. However, in this blog post, we will be discussing how
it can be accurately performed using Python.
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Python is a high-level programming language that is widely used for the creation of
tools, websites, etc. But today, you will uncover it’s another capability.
A Step-by-Step Guide for Performing Text Data Extraction
Using Python
Extracting text or data from different sources like images, receipts, etc. using
Python requires following the right steps that we have discussed below in complete
detail.
First Download & Install the Text Extraction Libraries:
The first step for you is to download and install essential Python libraries that will
be responsible for performing text extraction from the input picture or receipt.
Python libraries you need to install are:
● OpenCV – also known as CV2, is a popular Python library that is known for
performing various computer tasks such as image processing, etc.
● Pytesseract – It is basically a tool or engine that is powered by Optical
Character Recognition (OCR) technology to extract editable text from input
images with maximum accuracy.
● Pillow (PIL) – a special library that provides image manipulation and analysis
capabilities to Python.
● TextExtract – This is also a library that is capable of getting text from
different sources including pictures.
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You have to download all these libraries on your device (laptop or PC). To do so, you
can refer to Python’s official website. When the downloading is done, complete the
installation process using this prompt.
pip install opencv-python pytesseract pillow textract
Import the Libraries into the Code Editor:
Now, it is time to import the installed libraries into the code editor you are using.
The process is quite simple. For your maximum ease, below we have written the
prompt that you can use for importing.
import cv2
import pytesseract
from PIL import Image
Upload the Image:
Once you are done with library importing, you can proceed towards the image
uploading process. Mention “Name” against which the required image is saved on
your computer. You can also consider providing a complete address for ease of
image location.
The prompt you need to write for image uploading is.
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# Load the image
img = cv2.imread('image.jpg')
Preprocess the Required Image (Optional):
Preprocessing is a stage in the text extraction process that involves removing any
sort of distortions, noises, etc. from the input image to ensure quick and accurate
extraction. So, if you also want to do so with your input picture, then you can follow
this step, otherwise ignore it.
The OpenCV library will come into play to preprocess the given image. It will first
turn the uploaded picture in greyscale so that, other Python libraries (such as
Pytesseract, etc.) can quickly differentiate between letters and characters.
If needed, you can also apply a threshold on the input image. This process involves
creating a binary version of the given picture using black and white. Below, we have
written the Python code that you need to write to perform image preprocessing.
# Preprocess the image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) thresh =
cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
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Start Extraction
Finally, the step has arrived that we all are waiting for. Here the Pytesseract library
will be in action. It will effectively extract all the text from the given image without
compromising on accuracy. The command you will need to write is as follows:
# Extract text
text = pytesseract.image_to_string(image.jpg)
# Print the extracted text
print(text)
These are a few steps that you need to follow to perform text
extraction using Python.
But keep in mind that a single mistake in the code (even a missing comma or
colon) can lead to errors, so be careful while writing. Here, it would be great if you
go for advanced tools like Imagetotext.info. Such tools are trained on Python
algorithms and can help you automatically perform extraction within seconds.
Having said that, below is a quick demonstration of the said tool.
Demonstration of the Python-Trained Imagetotext.info
To show you how well Python-coded tools can work, we gave Imagetotext.info the
following image to extract text from.
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Once the image was provided and the tool processed it, here’s the output we got:
As you can see, tools like Imagetotext.info can accurately extract text from images.
So, if you can’t do it using OpenCV, Pytesseract, Pillow, and TextExtract libraries,
you can go down this easy road.
Wrapping Up
Text data extraction is a hectic and time-consuming task if done manually. That’s
not the case now, thanks to Python. This high-level programming can be used to
extract text from images accurately. In this detailed blog post, we have explained
the step-by-step procedure and code examples for maximum understanding.
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