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Journal of Electronic Imaging • Open Access

Vehicle license plate recognition using visual attention model and deep learning
Author(s): Di Zang; Zhenliang Chai; Junqi Zhang; Dongdong Zhang; Jiujun Cheng

Paper Abstract

A vehicle’s license plate is the unique feature by which to identify each individual vehicle. As an important research area of an intelligent transportation system, the recognition of vehicle license plates has been investigated for some decades. An approach based on a visual attention model and deep learning is proposed to handle the problem of Chinese car license plate recognition for traffic videos. We first use a modified visual attention model to locate the license plate, and then the license plate is segmented into seven blocks using a projection method. Two classifiers, which combine the advantages of convolutional neural network-based feature learning and support vector machine for multichannel processing, are designed to recognize Chinese characters, numbers, and alphabet letters, respectively. Experimental results demonstrate that the presented method can achieve high recognition accuracy and works robustly even under the conditions of illumination change and noise contamination.

Paper Details

Date Published: 4 May 2015
PDF: 10 pages
J. Electron. Imaging. 24(3) 033001 doi: 10.1117/1.JEI.24.3.033001
Published in: Journal of Electronic Imaging Volume 24, Issue 3
Show Author Affiliations
Di Zang, Tongji Univ. (China)
Ministry of Education (China)
Zhenliang Chai, Tongji Univ. (China)
Ministry of Education (China)
Junqi Zhang, Tongji Univ. (China)
Ministry of Education (China)
Dongdong Zhang, Tongji Univ. (China)
Ministry of Education (China)
Jiujun Cheng, Tongji Univ. (China)
Ministry of Education (China)


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