Classification of defaced occlusion plates based on convolutional neural network

Sen Zhang; Jingle Zhang; Jie Li; Shuai Chen

doi:10.1117/12.2574415

24 June 2020 Classification of defaced occlusion plates based on convolutional neural network

Sen Zhang, Jingle Zhang, Jie Li, Shuai Chen

Proceedings Volume 11526, Fifth International Workshop on Pattern Recognition; 1152605 (2020) https://doi.org/10.1117/12.2574415
Event: Fifth International Workshop on Pattern Recognition, 2020, Chengdu, China

Abstract

As one of the important components of intelligent transportation, license plate recognition plays an irreplaceable role in people's daily life. For example, illegal vehicles often escape from punishment because of the number plate defacement or intentional occlusion, which further increases the difficulty of law enforcement. Therefore, it is significant for automatic recognition system to improve the identification efficiency of the contaminated or occluded license plate. This paper mainly focuses on the recognition of occlusion number plate. License plates can be divided into four categories: normal number plate, partial occlusion number plate, complete occlusion number plate and unsuspended number plate. The traditional OCR algorithm has a high accuracy in the recognition of Chinese characters, characters and numbers. Although the detection of normal and partial occlusion plates also shows a good recognition in the case of OCR, the recognition of complete occlusion and unsuspended license plates is still very poor. With the development of artificial intelligence, it is possible to identify all the sheltered and unsuspended plates better. Combining with the advantages of traditional algorithms, this paper uses traditional OCR and current deep learning algorithm to optimize the recognition effect of stained license plate.

Citation Download Citation

Sen Zhang, Jingle Zhang, Jie Li, and Shuai Chen "Classification of defaced occlusion plates based on convolutional neural network", Proc. SPIE 11526, Fifth International Workshop on Pattern Recognition, 1152605 (24 June 2020); https://doi.org/10.1117/12.2574415

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