License Plate Recognition Through Color Feature Extraction-Based Segmentation

This license plate recognition system, developed internationally, employs color feature extraction and segmentation as its core methodology, with an embedded neural network for enhanced processing. The algorithm analyzes the distinctive color characteristics of license plates to isolate them from the input image, followed by character segmentation and recognition. The neural network component demonstrates robust learning capabilities, continuously optimizing recognition accuracy and processing speed through iterative training. The system implements key functions including color space conversion (e.g., RGB to HSV for better color isolation), morphological operations for noise reduction, and template matching or OCR techniques for character recognition. Additionally, it features a user-friendly interface that simplifies the import and processing of license plate images, supporting various image formats through standard OpenCV or similar computer vision libraries. With broad applicability, this solution can be effectively deployed in traffic management systems, vehicle surveillance infrastructure, and security applications.