Dynamic time sequence convolutional neural network-based license plate recognition method

Abstract

The invention discloses a dynamic time sequence convolutional neural network-based license plate recognition method. The method comprises the following steps of: reading an original license plate image; carrying out license plate angle correction to obtain a to-be-recognized license plate image; inputting the to-be-recognized license plate image into a previously designed and trained convolutionalneural network so as to obtain a feature image and time sequence information, wherein the feature image comprises all the features of the license plate; and carrying out character recognition, inputting the feature image into a convolutional neural network of a long and short-term memory neural network layer on the basis of time sequence information of the last layer so as to obtain a classification result, and carrying out decoding by utilizing a CTC algorithm so as to obtain a final license plate character result. According to the method, vision modes are directly recognized from original images through using convolutional neural networks, self-learning and correction are carried out, the convolutional neural networks can be repeatedly used after being trained for one time, and the timeof single recognition is in a millisecond level, so that the method can be applied to the scenes needing to recognize license plates in real time. The dynamic time sequence-based long and short-termneural network layer is combined with CTC algorithm-based decoding, so that recognition error problems such as leak detection and repeated detection are effectively avoided, and the algorithm robustness is improved.

Description

technical field [0001] The invention belongs to the fields of computer vision, digital image processing and deep learning, and in particular relates to a license plate recognition method of a dynamic temporal convolutional neural network. Background technique [0002] License plate recognition has always been a research hotspot in modern intelligent transportation systems. In the past, the most commonly used license plate recognition methods were traditional template matching and feedforward neural networks. The method of template matching is as follows: first, binarize the segmented license plate characters, and scale its size to the size of the template in the character database, then match all the templates, and select the best match as the result. This method is easy to deal with local transformations, but its application is limited. The main reason is that it is easily affected by interference noise. Matching calculations are huge and difficult in real time. The forwa...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the deficiencies of the existing technology, a license plate recognition method based on dynamic temporal convolutional neural network is proposed to solve the problems of low accuracy and wrong recognition results of license plate recognition results with different character lengths

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