Optical Character Recognition in Python: A Complete Guide to OCR Libraries and Keras-OCR

In today’s digital world, extracting text from images and scanned documents has become a crucial task. Whether it's digitizing books, extracting details from invoices, or automating data entry, Optical Character Recognition (OCR) plays a vital role in converting printed or handwritten text into machine-readable formats.

OCR technology is widely used in applications like document processing, license plate recognition, text extraction from images, and even in assistive technologies for the visually impaired. With advancements in machine learning and deep learning, modern OCR systems are now more accurate and efficient than ever.

Tesseract, EasyOCR, and Keras-OCR are popular OCR libraries for Python that enable text extraction from images with high accuracy.

In this blog, we will explore how to implement optical character recognition in Python, compare different OCR libraries for Python, and use Keras-OCR Python to detect and recognize text in images.

What is Optical Character Recognition (OCR)?

Optical Character Recognition (OCR) is a technology that enables computers to extract and convert text from images, scanned documents, and handwritten notes into machine-readable text. It eliminates the need for manual data entry by automatically recognizing and processing text from visual sources.

Also Read: What is RAG in LLM? The Future of Knowledge-Enhanced AI

How Does OCR Work?

OCR technology follows a structured process to identify and extract text from images:

1. Image Preprocessing – The image is cleaned by applying techniques like grayscale conversion, thresholding, and noise reduction to enhance text visibility.
2. Text Detection – The system identifies regions in the image containing text.
3. Character Recognition – Each character or word is analyzed using pattern matching or machine learning models.
4. Post-processing – The extracted text is refined to correct errors and improve accuracy.

Types of OCR

OCR technology is classified into different types based on its capabilities:

1. Traditional OCR – Recognizes printed text using pattern-matching techniques.
2. Intelligent Character Recognition (ICR) – Recognizes handwritten text using deep learning.
3. Optical Mark Recognition (OMR) – Detects checkboxes, bubbles, or marks in forms.

Why is OCR Important?

OCR has transformed industries by making text processing more efficient. Some key benefits include:

1. Automates data entry and reduces human effort.
2. Improves accessibility for visually impaired individuals.
3. Enables text searchability in scanned documents.‍
4. Speeds up document processing in finance, healthcare, and legal fields.

Python provides multiple libraries to implement OCR effortlessly. Some of the most popular OCR libraries for Python include:

1. Tesseract OCR – An open-source OCR engine developed by Google
2. EasyOCR – A deep learning-based OCR library supporting multiple languages‍
3. Keras-OCR – A powerful text detection and recognition framework using deep learning

Popular OCR Libraries for Python

Python offers several powerful OCR libraries that simplify text extraction from images. Each library has its own strengths and is suitable for different use cases. Let’s explore some of the most widely used OCR libraries for Python.

1. Tesseract OCR

Developed by: Google

Best for: General-purpose OCR, printed and handwritten text recognition

Key Features:

- Open-source and widely used

- Supports multiple languages

- Works well with image preprocessing techniques

Limitations:

- Struggles with noisy or distorted images

- Requires additional preprocessing for best accuracy

2. EasyOCR

Developed by: Jaided AI

Best for: Multi-language OCR with deep learning-based accuracy

Key Features:

- Pre-trained deep learning models for 80+ languages

- Faster and easier to use than Tesseract

- Works well with both printed and handwritten text

Limitations:

- Slightly heavier on system resources

- May not perform well on very complex backgrounds

3. Keras-OCR

Developed by: Community contributors based on Keras and TensorFlow

Best for: Keras OCR Python is a deep learning-based library that enables accurate text detection and recognition in images. It’s best for deep learning-based text detection and recognition.

Key Features:

- Uses convolutional neural networks (CNNs) for improved accuracy

- Handles complex images with text in different orientations

- Works well for real-time OCR applications

Limitations:

- Requires a GPU for optimal performance

- More complex to set up compared to Tesseract and EasyOCR

Setting Up OCR in Python

Before implementing optical character recognition in Python, we need to install and configure the necessary libraries. In this section, we'll set up Tesseract OCR, EasyOCR, and Keras-OCR Python for text extraction.

Step 1: Install Required Libraries

We’ll use pip to install the required OCR libraries:

1. pytesseract – Python wrapper for Tesseract OCR
2. easyocr – A deep learning-based OCR library
3. keras-ocr – A deep learning-based text detection and recognition framework
4. opencv-python – For image preprocessing
5. matplotlib – For displaying images

Step 2: Setting Up Tesseract OCR

Tesseract requires additional installation because it is a standalone OCR engine.

- For Windows: Download and install Tesseract-OCR

- For Linux (Ubuntu/Debian):

Once installed, find the Tesseract path and add it to your Python script:

This should output the installed Tesseract version, confirming a successful installation.

Step 4: Setting Up EasyOCR and Keras-OCR

Unlike Tesseract, EasyOCR and Keras-OCR do not require additional dependencies. Simply importing them should work:

Now that we have installed and configured these OCR libraries for Python, we can start implementing OCR in the next sections.

Also Read: Building and Implementing Effective NLP Models with Transformers

Implementing OCR with Tesseract

Now that we have set up Tesseract OCR, let's implement it in Python to extract text from images. Tesseract is an open-source OCR engine developed by Google, widely used for text recognition in scanned documents and images.

Step 1: Load and Preprocess the Image

Before passing an image to Tesseract, it's essential to preprocess it for better accuracy. This includes converting the image to grayscale, applying thresholding, and removing noise. Let's assume we have the following image (sample_text.png).

Step 2: Understanding the Output

The image_to_string() function extracts text from the image and returns it as a string. The extracted text may look like this:

Hello World!

Welcome to Optical Character Recognition (OCR).

‍

If the text is not clear, try additional preprocessing techniques such as Gaussian blur, dilation, or erosion to improve results.

Step 3: Configuring OCR for Different Languages

By default, Tesseract supports multiple languages. To specify a language, use the -l flag in the image_to_string() function.

For other languages like Hindi or French, install additional language packs and specify the language code:

Step 4: Extracting Text from a Specific Region

If you want to extract text from a specific part of an image, you can use bounding boxes.

‍

This will highlight the detected text regions in the image.

Limitations of Tesseract OCR

While Tesseract is a powerful tool, it has some limitations:

- Struggles with handwritten text

- Requires clean images for better accuracy

- Sensitive to font variations and background noise

To overcome these challenges, deep learning-based OCR solutions like Keras-OCR Python and EasyOCR can be used for better results, especially on complex images.

Using Keras-OCR for Text Detection and Recognition

Tesseract OCR works well for simple printed text, but it struggles with noisy backgrounds, distorted fonts, or non-uniform text alignment. Keras-OCR, a deep learning-based OCR system, is designed to handle these challenges effectively.

Unlike Tesseract, which is rule-based, Keras-OCR Python uses convolutional neural networks (CNNs) to detect and recognize text from complex images. With Keras OCR Python, you can easily extract text from complex backgrounds using a pre-trained pipeline.

Step 1: Installing and Importing Keras-OCR

Ensure you have Keras-OCR installed. If not, install it using:

Now, import the required modules:

Step 2: Load the Keras-OCR Pipeline

Keras-OCR provides a pre-trained pipeline that detects and recognizes text in images. Let's initialize it:

Step 3: Load an Image and Perform OCR

We can now load an image and let Keras-OCR process it.

Step 4: Understanding the Output

The Keras-OCR Python pipeline detects and recognizes text while providing bounding boxes around the text regions. The extracted text is returned in a list format:

[

    ('Hello', [(35, 25), (120, 25), (120, 60), (35, 60)]),

    ('World!', [(130, 25), (220, 25), (220, 60), (130, 60)])

]

This output includes:

- Recognized text ("Hello", "World!")

- Bounding box coordinates for each detected word

Step 5: Visualizing Text Bounding Boxes

If you want to visualize the detected text regions on your image:

‍

This will display the image with text boxes drawn around the detected words.

Step 6: Processing Multiple Images

Keras-OCR can process multiple images at once:

Advantages of Keras-OCR

1. Works well with noisy, rotated, or distorted text
2. Uses deep learning-based text detection for higher accuracy
3. Supports multiple languages
4. Can handle text in complex backgrounds

Limitations

1. Requires GPU acceleration for best performance
2. Heavier than Tesseract OCR in terms of memory and processing power

Now that we've explored Keras-OCR, let's explore EasyOCR, another deep learning-based OCR library that provides multilingual support with a simple interface.

Also Read: The Differences Between Neural Networks and Deep Learning Explained

Implementing OCR with EasyOCR

While Tesseract OCR and Keras-OCR are powerful tools, they have some limitations—Tesseract struggles with complex images, and Keras-OCR requires deep learning expertise and a GPU for optimal performance.

To overcome these challenges, we can use EasyOCR, an efficient, deep-learning-based OCR library that supports over 80 languages and works well with both printed and handwritten text.

Step 1: Installing and Importing EasyOCR

First, install EasyOCR using:

Now, import the necessary modules:

Step 2: Initializing the EasyOCR Reader

EasyOCR requires specifying the language(s) for text recognition. Let’s initialize the model:

If you want to recognize text in multiple languages, you can pass additional language codes, e.g., English + Hindi:

Step 3: Reading and Processing an Image

Now, let’s load an image and run OCR:

Step 4: Visualizing OCR Output

EasyOCR provides bounding box coordinates for each detected word. We can overlay these on the image:

Step 5: Processing Multiple Images

You can process multiple images in a loop:

Advantages of EasyOCR

1. Supports handwritten and printed text
2. Works well with noisy, rotated, or distorted text
3. Supports over 80 languages
4. More lightweight than Keras-OCR

Limitations

1. Requires a decent GPU for fast processing
2. Might misinterpret cursive or overlapping text

Choosing the Right OCR Library for Your Use Case

Now that we’ve explored Tesseract OCR, Keras-OCR, and EasyOCR, let’s compare them to help you decide the best library for your specific needs.

Choosing the right OCR library for Python depends on your use case, whether it's document scanning, handwriting recognition, or real-time text detection.

When to Use Each OCR Library

Use Tesseract OCR if:

1. You need a lightweight, open-source OCR solution.
2. Your images have clear, printed text.
3. You don’t have a GPU or deep learning setup.

Use Keras-OCR if:

1. You need end-to-end scene text detection and recognition.
2. Your images contain text within natural scenes (e.g., street signs, product labels).
3. You are comfortable with deep learning models.

Use EasyOCR if:

1. You need to recognize handwritten and printed text.
2. You require multilingual OCR support.
3. You have access to a GPU for better performance.

OCR technology has advanced significantly, and choosing the right tool depends on your use case.

1. If you want a quick, lightweight solution, Tesseract OCR is your best bet.
2. If you’re working with complex images or scene text, Keras-OCR will help.
3. If you need handwritten text recognition, EasyOCR is the way to go.

For real-world applications, combining multiple OCR models with image preprocessing can improve accuracy.

Also Read: What is Chatbots and What Are They Used For in Modern Businesses?

Real-World Applications of OCR in Python

Optical Character Recognition (OCR) is widely used across industries to extract text from images and scanned documents. By leveraging Python-based OCR libraries, businesses can automate processes and improve efficiency. Let’s explore some real-world applications where OCR plays a crucial role.

1. Document Digitization & Automation

OCR enables businesses to convert physical documents into editable and searchable digital formats.

- Use case: Scanning invoices, contracts, and receipts for automated processing.

- Example: Banks use OCR to digitize cheques and extract details automatically.

2. License Plate Recognition

OCR is commonly used in automatic number plate recognition (ANPR) for vehicle tracking and law enforcement.

- Use case: Toll booths and smart traffic monitoring systems.

- Example: Detecting stolen vehicles using surveillance cameras.

3. Automated Data Entry

OCR reduces manual data entry efforts by extracting text from forms, business cards, and ID cards.

- Use case: Hospitals use OCR to digitize patient records.

- Example: Extracting customer details from scanned KYC documents.

4. Assistive Technology for Visually Impaired Users

OCR is integrated into screen readers and other assistive technologies to help visually impaired individuals read printed text aloud.

- Use case: Mobile apps like Google Lens read restaurant menus or books aloud.

- Example: AI-powered text-to-speech applications convert text from images into voice output.

5. Product Label & Expiry Date Detection

Retailers and supermarkets use OCR to detect product labels, barcodes, and expiry dates.

- Use case: Automating inventory management and expiry tracking.

- Example: OCR systems alert store managers when products near their expiration date.

6. Translation of Foreign Text

OCR combined with machine translation helps translate foreign-language text from images.

- Use case: Mobile apps like Google Translate extract and translate text in real time.

- Example: Tourists use OCR to read menu cards, signboards, and travel documents in different languages.

7. Legal & Compliance Applications

Law firms and government agencies use OCR to digitize legal documents for better searchability.

- Use case: Extracting important clauses from contracts.

- Example: Courts use OCR to process case files and legal documents faster.

How to Integrate OCR into Your Projects

By now, you’ve learned how to use Tesseract OCR, Keras-OCR, and EasyOCR. Here are some ideas to build practical OCR applications in Python:

1. Automated Invoice Processing – Extract invoice numbers, dates, and totals from scanned invoices.
2. Smart Attendance System – Recognize names from ID cards for check-in automation.
3. Handwritten Note Digitization – Convert handwritten lecture notes into editable text.
4. AI-Powered OCR Chatbot – Allow users to upload images and get extracted text via a chatbot.
5. OCR-Based Resume Screening – Extract text from resumes and automate job application processing.

Checkout 10 Essential Python Libraries for Machine Learning and understand how machine learning libraries help in various use cases.

Conclusion

OCR in Python has revolutionized how businesses and individuals extract text from images. Whether you need document automation, real-time translation, or assistive technology, OCR libraries like Tesseract, Keras-OCR, and EasyOCR can be used to build powerful solutions.

If you're new to OCR, start with Tesseract for basic text extraction. If you need deep learning-based scene text detection, explore Keras-OCR or EasyOCR.

Try implementing OCR in your own projects. Experiment with image preprocessing to improve accuracy. Explore OCR with deep learning for more advanced applications.