Text Detection and Recognition with OCR and OpenCV
Have you ever wondered how your smartphone can magically recognize text from images? Well, it’s all thanks to OCR (Optical Character Recognition) technology! In this blog, we will explore about Tesseract engine, and learn how to enhance its performance for maximum accuracy. Additionally, we’ll know a few capabilities of OpenCV to detect text in images and extract it seamlessly. So, let’s get started in and unlock the secrets of text detection and recognition!
What is OCR?
OCR is Optical Character Recognition, a technology that allows computers to extract text from images or scanned documents. It’s like teaching your computer to read!
If you wanna read more on OCR please redirect yourself to this blog:
OCR(Optical Character Recognition)
Tesseract:
Tesseract is an open source Optical Character Recognition (OCR) engine, initially developed by Hewlett Packard (HP) and currently supported by Google. It’s designed to extract text from images or scanned documents, essentially teaching computers to “read” text from visual sources. Tesseract is highly versatile, supporting over 100 languages and offering advanced features such as LSTM neural network mode for improved accuracy. With its wide range of applications, Tesseract has become a popular tool for tasks like digitizing books, extracting information from images, and enabling text search in scanned documents.
Pytesseract:
Pytesseract is a Python wrapper for Tesseract, which means it provides a convenient interface for using the Tesseract OCR engine in Python code. Essentially, Pytesseract allows you to get the power of Tesseract directly within your Python scripts, making it easier for you to integrate OCR capabilities into your projects.
By using Pytesseract, you can perform tasks such as reading text from images, extracting information from scanned documents, and automating data entry processes. It simplifies the process of interacting with Tesseract by providing a more Pythonic interface, allowing you to focus on your application logic rather than dealing with low-level details.
Here’s how Pytesseract works:
1. Installation: First, you need to install Pytesseract and Tesseract on your system. You can do this using pip for Pytesseract and your package manager for Tesseract.
2. Integration: Once installed, you can import Pytesseract into your Python script and start using its functions. Pytesseract provides a simple function, `image_to_string()`, which takes an image as input and returns the extracted text.
3. Configuration: You can also configure Pytesseract by passing additional parameters to the `image_to_string()` function. These parameters allow you to customize the OCR process according to your specific requirements, such as language selection, page segmentation mode, and engine mode.
4. Text Extraction: After configuring Pytesseract, you can pass images to the `image_to_string()` function and extract text from them. Pytesseract internally invokes the Tesseract OCR engine to perform the text recognition process and returns the extracted text as a string.
By using Pytesseract, you can easily access the power of Tesseract OCR in your Python applications, making it more accessible and convenient to perform text recognition tasks. Whether you’re building a document processing pipeline, automating data extraction from images, or creating a text-based search engine, Pytesseract simplifies the integration of OCR functionality into your projects.
Tips for Maximum Tesseract Performance:
0) Know Your Data: Understand the quality of the images you’re processing. Check for issues like cropping, distortion, or improper formatting.
1)Configure Parameters: Fine-tune Tesseract parameters like trained data, page segmentation mode, engine mode, and character whitelist for optimal performance.
2)Correct Skew: Correct image rotation before passing it to Tesseract to ensure accurate recognition.
3) Don’t Crop Too Close: Provide background space around text areas to enhance OCR performance.
Postprocess OCR Results:
Implement postprocessing techniques like character substitution, dictionary checks, and punctuation handling to improve accuracy.
Getting Hands-on with Pytesseract
If you prefer coding in Python, Pytesseract is your best friend! It’s a Python wrapper for Tesseract, making text recognition a breeze. Let’s write some code to recognize text from an image:
import cv2
import pytesseract
image = cv2.imread('test_image.jpg')
text = pytesseract.image_to_string(image)
print(text)Text Detection Using OpenCV
OpenCV, a powerful computer vision library. We’ll focus on the traditional method using contours. And with the further blogs, we will dig it deeper. For today, I’ll be explaining its applications so that with moving further you can have an idea of what you are going to learn.
Traditional Text Detection Steps:
0) Preprocessing: Convert the image to grayscale, apply blur, and thresholding. 1)Finding Contours: Detect contours in the thresholded image. 2) Text Detection: Extract text from contours using Tesseract.
Applications of OpenCV:
There are lots of applications that are solved using OpenCV, some of them are listed below:
0. face recognition
1.Automated inspection and surveillance
2. number of people — count (foot traffic in a mall, etc)
3. Vehicle counting on highways along with their speeds
4. Interactive art installations
5. Anomaly (defect) detection in the manufacturing process (the odd defective products)
7. Street view image stitching
8. Video/image search and retrieval
9. Robot and driver-less car navigation and control
10. object recognition
11. Medical image analysis
12. Movies — 3D structure from motion
13. TV Channels’ advertisement recognition
Conclusion
In this blog, we’ve looked into OCR and text detection, learning about Tesseract, Pytesseract, and OpenCV. We’ve even tried out some code with them. Now that you understand the basics of OCR and how to tweak Tesseract for better performance, along with using OpenCV for text detection we didn’t dig it deeper but we will do it eventually, what next you can do is start doing some text recognition projects! Give it a try and see what cool stuff you can build. Happy coding and extracting text!
In the next BLOG, we will be more focused on OpenCV, Arigato :)
