This project is a simple web based license plate detection system implemented in Python using OpenCV, pytesseract, and Matplotlib libraries.

The goal of this project is to develop a system that can detect license plates in images and extract the license plate numbers from the detected plates. The system uses computer vision techniques for image processing and optical character recognition (OCR) for extracting text from the detected license plates.

You need to have the following libraries installed in your Python environment to run this project:

• OpenCV
• pytesseract
• Matplotlib

You can install these libraries using pip:

pip install opencv-python
pip install pytesseract
pip install matplotlib

1. Clone the repository to your local machine:

   git clone https://github.com/your-username/cv_project_1.git

2. Navigate to the project directory:

   cd cv_project_1

3. Run the main Python script:

      flask --app app --debug run

4. The script will process the input image, detect license plates, and display the result with the extracted plate numbers.

1. Image Input: The system takes an input image containing vehicles with license plates.

2. License Plate Detection: OpenCV is used to detect the location of license plates within the image using image processing techniques such as edge detection and contour analysis.

3. License Plate Recognition: pytesseract is employed to recognize and extract the text from the detected license plates.

4. Result Visualization: Matplotlib is used to visualize the original image with the detected license plates and the extracted license plate numbers.

• license_plate_detection.py: Main Python script for the license plate detection system.
• input_image.jpg: Sample input image for testing the system.

• OpenCV: Open Source Computer Vision Library
• pytesseract: Ready-to-Use OCR with 40+ languages supported
• Matplotlib: Python plotting library

This project is licensed under the MIT - see the LICENSE file for details.

Feel free to contribute to this project by forking the repository and submitting a pull request. Any suggestions and improvements are welcome.