License Plate Recognition: Algorithm Implementation and Technical Analysis

This undergraduate thesis investigates license plate recognition systems, examining technical challenges and proposing solutions to improve recognition accuracy and processing efficiency. The research provides comprehensive analysis of fundamental principles, including image preprocessing techniques like Gaussian filtering for noise reduction, edge detection algorithms such as Canny operator for plate localization, and character segmentation methods using vertical projection analysis. The study details OCR (Optical Character Recognition) implementation through template matching or CNN-based character classification, discussing parameter optimization for various lighting conditions and plate orientations. Additionally, the paper explores historical development milestones, current industry applications in traffic management systems, and future trends integrating deep learning architectures like YOLO for real-time detection. By implementing techniques such as HSV color space conversion for plate region extraction and morphological operations for character enhancement, this research contributes to intelligent transportation systems, vehicle security applications, and automated traffic monitoring solutions. The thesis aims to provide practical guidance for researchers through documented code structures and performance evaluation metrics, serving as a technical reference for further innovation in automated license plate recognition technology.