License Plate Recognition Method And Application Based On Deep Learning Theory

As one of the core contents of the intelligent traffic management system,license plate recognition reflects the development speed and technical level of the intelligent transportation system.The traditional license plate recognition method is divided into three modules:license plate location,character segmentation and character recognition.The error between modules easily affects the subsequent processing,which in turn affects the final recognition rate.At the same time,the traditional license plate recognition technology has higher requirements on image quality,and is sensitive to factors such as vehicle motion,illumination change,shooting angle,etc.,and the recognition rate is affected when the license plate image appears blurred,the light is too dark and tilted.At present,deep learning algorithms have made great progress in the fields of image recognition and speech recognition.Aiming at the shortcomings of the traditional recognition method,this paper proposes a new license plate recognition method based on the deep learning theory,which changes the three parts of license plate location,character segmentation and character recognition into two parts:license plate location and license plate recognition.The license plate recognition process at the end improves the recognition rate of the license plate in a complex environment.(1)The license plate feature extraction and image processing methods under the deep learning framework are studied.The robustness of the traditional license plate recognition method is insufficient,and the deep learning theory is introduced into the license plate recognition method.In order to avoid the influence of character segmentation on character recognition,this paper uses the deep learning network model to identify the entire license plate characters end-to-end,and improves the generalization ability of the recognition algorithm.(2)In view of the shortcomings of the traditional license plate location method,this paper combines the image processing related technology and the maximum extreme value stabilization region algorithm,and draws on the idea of non-maximum value suppression algorithm to improve the traditional license plate location method;AlexNet’s network structure was modified to train the authenticity classification model of the license plate to improve the accuracy of the license plate location algorithm.(3)In view of the influence of character segmentation on subsequent recognition effects in traditional recognition methods,this paper studies the characteristics of convolutional neural networks and cyclic neural networks,proposes an end-to-end license plate character recognition model,and designs a convolutional neural network based on Two model structures of convolutional cyclic neural networks.Through the feature learning of a large number of complete license plate data samples,the identification of the entire license plate character area is realized by using the constructed recognition model.(4)The method of license plate location and character recognition proposed in this paper is verified by experiments.The experimental results show that the convolutional neural network model designed in this paper can effectively verify the authenticity of the license plate area and improve the accuracy of license plate location.Compare the two end-to-end The license plate character recognition model,based on the character recognition model of convolutional cyclic neural network,can achieve better recognition results for license plates in complex environments such as dark light,oblique angle and slight occlusion.At the same time,compared with the traditional license plate recognition method,the advantages of the algorithm in license plate location and character recognition are verified.On this basis,the integrated license plate location and license plate recognition process is realized to realize a complete license plate recognition system from inputting vehicle images to identifying license plate characters.