Abstract

Optical Character Recognition is a Python project that uses OCR to recognize text in images. The application features image processing, text extraction, and a CLI interface, demonstrating best practices in computer vision and automation.

Prerequisites

• Python 3.8 or above
• A code editor or IDE
• Basic understanding of OCR and computer vision
• Required libraries: pytesseractpytesseract, opencv-pythonopencv-python, numpynumpy

Before you Start

Install Python and the required libraries:

pip install pytesseract opencv-python numpy

pip install pytesseract opencv-python numpy

Getting Started

Create a Project

1. Create a folder named optical-character-recognitionoptical-character-recognition.
2. Open the folder in your code editor or IDE.
3. Create a file named optical_character_recognition.pyoptical_character_recognition.py.
4. Copy the code below into your file.

Write the Code

import cv2
import pytesseract
import numpy as np
 
class OpticalCharacterRecognition:
    def __init__(self):
        pass
 
    def recognize_text(self, image):
        text = pytesseract.image_to_string(image)
        print(f"Recognized text: {text}")
        return text
 
    def demo(self):
        img = np.zeros((100, 300, 3), dtype=np.uint8)
        cv2.putText(img, 'Python OCR', (5, 70), cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 3)
        self.recognize_text(img)
        cv2.imshow('OCR Demo', img)
        cv2.waitKey(1000)
        cv2.destroyAllWindows()
 
if __name__ == "__main__":
    print("Optical Character Recognition Demo")
    ocr = OpticalCharacterRecognition()
    ocr.demo()
 

import cv2
import pytesseract
import numpy as np
 
class OpticalCharacterRecognition:
    def __init__(self):
        pass
 
    def recognize_text(self, image):
        text = pytesseract.image_to_string(image)
        print(f"Recognized text: {text}")
        return text
 
    def demo(self):
        img = np.zeros((100, 300, 3), dtype=np.uint8)
        cv2.putText(img, 'Python OCR', (5, 70), cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 3)
        self.recognize_text(img)
        cv2.imshow('OCR Demo', img)
        cv2.waitKey(1000)
        cv2.destroyAllWindows()
 
if __name__ == "__main__":
    print("Optical Character Recognition Demo")
    ocr = OpticalCharacterRecognition()
    ocr.demo()
 

Example Usage

python optical_character_recognition.py

python optical_character_recognition.py

Explanation

Key Features

• OCR: Recognizes text in images.
• Image Processing: Prepares images for text extraction.
• Error Handling: Validates inputs and manages exceptions.
• CLI Interface: Interactive command-line usage.

Code Breakdown

1. Import Libraries and Setup OCR

import pytesseract
import cv2
import numpy as np

import pytesseract
import cv2
import numpy as np

2. Image Processing and Text Extraction Functions

def preprocess_image(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return gray
 
def extract_text(image):
    text = pytesseract.image_to_string(image)
    return text

def preprocess_image(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return gray
 
def extract_text(image):
    text = pytesseract.image_to_string(image)
    return text

3. CLI Interface and Error Handling

def main():
    print("Optical Character Recognition")
    # image = cv2.imread('text_image.jpg')
    # processed = preprocess_image(image)
    # text = extract_text(processed)
    print("[Demo] OCR logic here.")
 
if __name__ == "__main__":
    main()

def main():
    print("Optical Character Recognition")
    # image = cv2.imread('text_image.jpg')
    # processed = preprocess_image(image)
    # text = extract_text(processed)
    print("[Demo] OCR logic here.")
 
if __name__ == "__main__":
    main()

Features

• OCR: Text recognition and image processing
• Modular Design: Separate functions for each task
• Error Handling: Manages invalid inputs and exceptions
• Production-Ready: Scalable and maintainable code

Next Steps

Enhance the project by:

• Integrating with real image datasets
• Supporting advanced OCR algorithms
• Creating a GUI for OCR
• Adding real-time recognition
• Unit testing for reliability

Educational Value

This project teaches:

• Computer Vision: OCR and image processing
• Software Design: Modular, maintainable code
• Error Handling: Writing robust Python code

Real-World Applications

• Document Digitization
• Accessibility Tools
• AI Platforms

Conclusion

Optical Character Recognition demonstrates how to build a scalable and accurate OCR tool using Python. With modular design and extensibility, this project can be adapted for real-world applications in digitization, accessibility, and more. For more advanced projects, visit Python Central Hub.