License plate recognition method based on deep convolutional neural network

Abstract

The invention belongs to the technical field of image processing and mode recognition and particularly relates to a license plate recognition method based on a deep convolutional neural network. The method includes: performing license plate detection on vehicle images, performing image segmentation on detected license plates to obtain license plate characters, using the license plate characters as training samples to obtain a training sample block set, inputting the training sample block set into a deep auto-encoder to train the deep auto-encoder, using the trained deep auto-encoder as the convolution kernel of the convolutional neural network, extracting the convolution features of the training sample block set, performing pooling operation on the convolution features of the training sample block set to obtain feature vectors, performing normalization processing on the feature vectors, loading the feature vectors after the normalization processing into an SVM classifier to train the SVM classifier, and testing to-be-recognized vehicles. By the method, license plate recognition accuracy can be increased, and license plate character recognition rate and robustness can be increased when the license plate characters are located in severe environments.

Description

technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, in particular to a license plate recognition method based on a deep convolutional neural network. Background technique [0002] License plate recognition technology is a key technology in modern intelligent transportation systems and has been widely used in daily life. Today, deep learning has become a hot spot in the field of computer pattern recognition. Using deep learning algorithms can improve the recognition rate of license plate recognition. It is of great significance to improve the robustness of the license plate recognition system. [0003] The license plate recognition method in the prior art still has a low recognition rate for the license plate in bad weather, at night, and when the angle is too inclined. The character blur affects the feature extraction, and special character recognition is easily confused, such as 8 and B, 2 and Z, 0 and D, 5 and S...

AI Technical Summary

Problems solved by technology

[0003] The license plate recognition method in the prior art still has a low recognition rate for the license plate in bad weather, at night, and when the angle is too inclined. The character blur affects the feature extraction, and special character recognition is easily confused, such as 8 and B, 2 and Z, 0 and D, 5 and S, etc. Therefore, it is urgent to propose a license plate recognition method that improves the recognition rate and robustness of the license plate characters when the license plate characters are in a harsh environment

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