This project is a License Plate Recognition system that uses computer vision and machine learning techniques to detect and read license plates from images or video streams.

The project consists of two main components:

1. License Plate Detection: Uses a pre-trained Haar cascade classifier to detect license plates in an image or video stream.
2. Optical Character Recognition (OCR): Recognizes text from the detected license plates using OCR techniques.

• LicencePlateRecognition.py: A script that uses OpenCV to capture video from the webcam, detect license plates, and save the detected region of interest (ROI) as an image.
• ocr.ipynb: A Jupyter notebook that contains code for performing OCR on the detected license plates.
• model/haarcascade_russian_plate_number.xml: A Haar cascade XML file used for detecting license plates.
• plates/: A directory where images of detected license plates are saved.

• Python 3.x
• OpenCV
• NumPy
• Jupyter Notebook
• pytesseract (for OCR)

1. Clone the repository:

   git clone https://github.com/yourusername/LicensePlateRecognition.git
   cd LicensePlateRecognition
   
   

1. Ensure the Haar cascade XML file is in the model/ directory.

2. Run the LicencePlateRecognition.py script to start the webcam and detect license plates:

   python LicencePlateRecognition.py
   

3. Press 's' to save the detected license plate region as an image in the plates/ directory.

1. Open the ocr.ipynb notebook using Jupyter Notebook:

   jupyter notebook ocr.ipynb
   

2. Run the cells in the notebook to perform OCR on the detected license plate images.

Contributions are welcome! Please feel free to submit a Pull Request.

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

This complete Markdown file is ready to be used as `README.md` on GitHub. It includes headings, lists, and code blocks, all properly formatted for Markdown. Let me know if you need any further adjustments!