A license plate recognition system based on CNN neural network

In the context of advancing modern highway transportation and the automotive industry, license plate recognition technology has become crucial for fulfilling the demands of traffic and safety management within intelligent transportation systems. This article aims to use convolutional neural networks to accelerate the recognition of car license plates. Firstly, the collected images are preprocessed by filtering and denoising. Secondly, perform morphological operations on the image, use license plate correction, and use contour detection to determine the position of the license plate. Then adopt appropriate methods for character segmentation. Ultimately, convolutional neural networks are employed for the recognition of license plate characters. The model test results show that the comprehensive model accuracy of the license plate recognition system is 91.2%, with high accuracy. Compared with ordinary license plate recognition systems, it has higher corresponding speed and recognition ability. This improvement is significant for real-time applications. With the continuous progress of technology and the expansion of applications, license plate recognition will play a more important role in smart transportation, providing more efficient and intelligent solutions for the construction of smart cities.