License Plate Recognition Based on Neural Networks

License plate recognition technology based on neural networks is continuously evolving, achieving high-precision identification capabilities. This technology can be widely applied in areas such as traffic management, road surveillance, and parking lot management, enhancing operational efficiency and security. Key implementation steps typically include image preprocessing (e.g., grayscale conversion and noise reduction), license plate localization using edge detection or contour analysis, character segmentation via projection methods, and character classification employing convolutional neural networks (CNNs) or recurrent neural networks (RNNs). Additionally, as the technology advances, integrating license plate recognition with other systems—such as combining optical character recognition (OCR) with real-time tracking algorithms—enables extended functionalities like vehicle tracking and violation detection. This integration demonstrates promising application prospects for intelligent transportation systems.