Egyptian License Plate Recognization

This project leverages OpenCV and Python to detect license plates from images. The program processes an image to identify the license plate area using various image processing techniques.

Features:

• Image preprocessing to enhance the detection of license plates.
• Detection of license plates within an image.
• Developed a robust plate segmentation algorithm to accurately extract characters and digits from license plates under challenging conditions.
• Provides the option to select from various machine learning and deep learning models.
• Provides exclusive access to the designated vehicles only.

Dataset:

The dataset used for this project is publicly available in the repository: Car Plates Dataset

Installation:

Please ensure you have Python and the necessary libraries installed. To install the required libraries:

pip install -r requirements.txt

Access Control:

To Run the Grant/Remove access app:

   python -m streamlit run .\access_control.py

Run The Project

To run the project, use the following command:

python -m streamlit run .\GUI.py

Note: make sure you have navigated the terminal to the location of the project folder.

Pictures:

• access app:

• main app:

  • allowed vehicle example:

    

  • not allowed vehicle example:

    

Future Enhancements

• Real-Time Plate Recognition: Enable real-time recognition by integrating camera feeds, eliminating the need to upload pictures manually and allowing instant access checks.
• User Registration & Management: Implement a comprehensive user registration system with profile management and authentication features..
• Role-Based Access Control: Introduce role-based access control to limit access based on user roles and permissions, enhancing security.
• Mobile Application: Develop a mobile version of the application for easy access and convenience, enabling users to manage and do an instant search with the mobile camera.
• Cloud Integration: Integrate the solution with cloud platforms for seamless scaling, and high availability.

Contributing

We welcome contributions to enhance the project. Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature.
3. Submit a pull request with detailed explanations.