%A LI Lin, ZHAO Kai-yue, ZHAO Xiao-yong, WEI Shuai-qin and ZHANG Bing %T Contaminated and Shielded Number Plate Recognition Based on Convolutional Neural Network %0 Journal Article %D 2020 %J Computer Science %R 10.11896/JsJkx.191100089 %P 213-219 %V 47 %N 6A %U {https://www.jsjkx.com/CN/abstract/article_19148.shtml} %8 2020-06-16 %X As one of the important components of intelligent transportation,license plate recognition plays an irreplaceable role in people’s daily life.For example,in daily life,illegal vehicles often avoid punishment because of the number plate contamination and occlusion,which further increases the difficulty of law enforcement.Therefore,improving the recognition efficiency of contaminated license plate is still a crucial issue in today’s automatic recognition system.The paper mainly focuses on the recognition of shielded number plate.There are four main cases:normal number plate,partially shielded number plate,completely shielded number plate and not hanging plate.The traditional OCR algorithm has a high accuracy in the recognition of Chinese characters,characters and numbers.When it is applied to the recognition of license plates,although the detection of normal and partial shielded license plates shows a good recognition effect,the recognition effect of completely shielded number plates and not hanging license plates is still very poor.With the development of artificial intelligence,it is possible to get better recognition on completely shielded plates and not hanging plates.Therefore,combined with the advantages of traditional algorithms,this paper adopted OCR technology and the current deep learning algorithm to optimize the recognition effect of stained license plate.