Abstract: As the core of intelligent traffic management system,the research of license plate recognition technology has important business prospects.The traditional license plate character recognition method has the problem of complex feature extraction.As an efficient recognition algorithm,convolution neural network has a unique superiority in dealing with two-dimensional license plate image.When the traditional convolution neural network LeNet-5 identifies the license plate image,there is a series of problems such as less training data,redundancy of the fully connection layer and over-fitting of the network.A global intermediate pool (GMP-LeNet) network was designed,which utilizes the convolution la-yer instead of the fully connection layer.The 1*1 convolution kernel learning from the NIN network is used to reduce channel dimension.Then the global mean pool layer feeds the feature graph to the output layer after the dimension reducing directly.Experiments show that GMP-LeNet network can suppress the over-fitting phenomenon effectively with a faster recognition speed and the higher robustness.The final license plate recognition rate is close to 98.5%.

Key words: Convolution neural network, LeNet-5, License plate recognition, Over-fitting, Pooling