License Plate Detection and Recognition

This project demonstrates a license plate detection and recognition system using OpenCV, EasyOCR, and Python. The system detects the license plate in an image, extracts the text using optical character recognition (OCR), and overlays the detected text on the original image.

Table of Contents

• Introduction
• Installation
• Usage
• Applications
• Contributing
• License

Introduction

License plate detection and recognition is a technology used in various applications such as traffic management, security, and automation. This project utilizes OpenCV for image processing and EasyOCR for text recognition to build a simple but effective license plate recognition system.

Installation

To run this project, you need to have Python installed along with the required libraries. Follow the steps below to set up the environment:

1. Clone the repository:

   git clone https://github.com/yourusername/license-plate-detection.git
   cd license-plate-detection

2. Create and activate a virtual environment (optional but recommended):

   python -m venv env
   source env/bin/activate  # On Windows, use `env\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

To use the license plate detection and recognition system, run the provided Python script. Here's an example usage:

python detect_license_plate.py --image path/to/your/image.jpg

Applications

License plate detection and recognition can be applied in various fields, including:

• Traffic Management: Automated monitoring of vehicles for traffic regulation enforcement.
• Security: Surveillance systems to track vehicles entering or leaving a facility.
• Parking Management: Automated entry and exit systems in parking lots.
• Toll Collection: Automated toll booths that recognize vehicle license plates for payment processing.

Contributing

Contributions are welcome! If you'd like to contribute to this project, please fork the repository and use a feature branch. Pull requests are warmly welcome.

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

lisence

Distributed under the MIT License. See LICENSE for more information.

You can place this content in a file named `README.md` in your project directory. This file provides an overview of your project, how to set it up, and how to use it. The interactive elements (such as the example code) help users understand the functionality and apply it to their own images.