Top Python Libraries for Image Text Recognition

As a developer, you’ve likely come across Python libraries designed to extract and recognize text from images. These tools are invaluable for tasks like digitizing documents, processing receipts, or building Optical Character Recognition (OCR) systems. Python, known for its versatility and widespread use in development, data analysis, and machine learning, offers powerful libraries tailored for text recognition. Tools such as Tesseract, EasyOCR, and OpenCV streamline this process, enabling developers to focus on crafting innovative and efficient solutions. With these libraries, extracting text from images becomes faster, more accurate, and accessible across various applications and industries.

In this blog post, we are going to walk you through the top Python libraries for image text recognition in 2025. If you work on Python alternatives like Java, PHP, etc., knowing the basics will be beneficial.

3 Best Python Libraries for Image Text Recognition

Before delving into them we wanted to share a key reason why developers rely on Python when it comes to developing tools like an image-to-text converter. That is because of Python frameworks. They provide them with a layer of abstraction which enables them to write less code, making the development process easier. 

Here are the top 3 Python libraries for image text recognition that developers must know about. 

1. Tesseract

Tesseract – it is a popular Python library also known as an open-source OCR engine being maintained by Google). Here’s the list of features boasted by this Python library. 

• Simple interface
• Easy to integrate with Python
• Supports over 100 languages
• Handle multiple image formats
• Support document formats like PDF
• Allow developers to customize parameters i.e., page segmentation modes and language models

Now that we have covered the basics about the Tesseract library. Let us show you how you can install it. 

Steps for Installing Tesseract Library

Without any fluff let’s straight jump into the steps. 

Start by installing the Tesseract Python OCR engine on your device. After this, you have to install the Pytesseract library (crucial). You can install it using the pip command:

pip install pytesseract

The next thing you should do is import the Pytesseract module into your Python script. For this run the command:

import Pytesseract

Now is the time to set the path for Tesseract. For this, you have to use a variable shared in the code below. 

pytesseract.pytesseract.tesseract_cmd = r'/path/to/tesseract'

The next step is performing the OCR functionality by running the command:

# Perform OCR on an image
text = pytesseract.image_to_string('image.jpg')

The above command will let the tool extract text from the image and save it as a text variable. 

Lastly, you have to print the results to display them. For this run the code we have shared below:

print(text)

Note: The above procedure is about only the installation and implementation of the Tesseract library and remember that the output can vary based on variables, language, complexity, etc.  

2. Easy OCR

It is another Python library that has the functionality of extracting text from images. Here’s the list of features boasted by Easy OCR. 

• Powered by deep learning model
• Supports multiple languages
• Able to detect handwritten or printed texts
• Smoothly handles text styles and orientations

Steps for Installing Easy OCR Library

To get started, first, make sure that Python is installed on your device. If it’s already installed simply open the command line or terminal and run the command that we have shared below using pip. 

pip install easyocr

The working of EasyOCR needs a few dependencies. Running the above command using pip will take care of that automatically. 

That’s it EasyOCR is installed and now your Python scripts can use it. To check whether it is recognizing text or not consider running the code that we have shared below. 

import easyocr

# Create an OCR reader object
reader = easyocr.Reader(['en'])

# Read text from an image
result = reader.readtext('image.jpg')

# Print the extracted text
for detection in result:
    print(detection[1])

3. OpenCV

The next library on our list is OpenCV which was created by Intel and is now managed by a developer community. Here are the key features of this library:

• Good for computer vision and machine learning
• Allows to manipulate images
• Good accuracy other than image text recognition i.e., object detection, face recognition, etc. 
• Boasts cross-platform compatibility feature
• Can be integrated with other Python libraries such as TensorFlow, SciPy, etc. 

Steps for Installing OpenCV Library

Run the below pip command to begin the installation of OpenCV. 

pip install opencv-python

Now, like we did for pytesseract, import OpenCV library to your Python script. Run the below command for this. 

import cv2

Give the command to start the image recognition. For this using cv2.imread() is recommended like below.

# Read an image from file
image = cv2.imread('image.jpg')

Next comes showing up the image. For displaying you have to use the functionality cv2.imshow() in a window.

# Display the image in a window
cv2.imshow('Image', image)

Now comes waiting for the user input. For this run the functionality below.

# Wait for a key press and close the window
cv2.waitKey(0)
cv2.destroyAllWindows()

OpenCV library allows preprocessing of the images for tasks like resizing, cropping, filtering, etc. For this, you have to run the functionality:

# Resize the image
resized_image = cv2.resize(image, (width, height))

# Convert the image to grayscale
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Apply Gaussian blur to the image
blurred_image = cv2.GaussianBlur(image, (5, 5), 0)

To further save the preprocessed images use the function “cv2.imwrite()” lie:

# Save the processed image to file
cv2.imwrite('processed_image.jpg', processed_image)

You can easily install and implement the OpenCV library for text recognition and extraction using all the steps shared above. 

Example of Image Text Recognition Tool

Now that we have walked you through 3 top libraries. We wanted to give you an example of a tool developed using one of the above Python libraries. 

The tool we are talking about is Imagetotext.io which is built using Tesseract and some other Python libraries. Below we have shared the screenshot of its interface. 

This online OCR tool can recognise the text inside images and let users extract it from them. When we talk about its features most of them are those that we mentioned earlier before installing the Tesseract library above. Here are some key among them:

• It is multilingual
• Support multiple image formats
• Allows to download the text extracted
• Can do batch processing of images, etc.

Bottom Line

As we are in 2025, we can expect that Python will be a go-to choice for developers because of its powerful libraries like those we shared above. These libraries offer a range of features, from multilingual support to advanced image processing capabilities. They simplify development while ensuring good performance. Try them to create innovative and efficient OCR tools.