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5 January 2019 Chinese license plate image database building methodology for license plate recognition
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Abstract
License plate image database is the most significant factor that supports the development of license plate recognition. We first present a general method for building a license plate image database and establish a Chinese license plate image database. There are 29,015 images included in the database, named SYSU. The main work includes: first, based on a quantitative analysis of the attributes of license plate images that affect license plate recognition, relational license plate image database models are established, which consist of function and performance dataset models; second, based on the function dataset models, we present a semiautomatic method that can extract the values of the attributes in the road monitoring image and establish the function datasets, which include type and provincial abbreviation variation images. To open the function datasets, we also propose a preservative deidentification method to balance privacy protection and attribute preservation. Third, based on the performance dataset models, we establish the performance datasets, which are digital processing images from the function image datasets, including resolution, average luminance, nonuniformity of luminance, horizontal rotation angle, vertical shear angle, horizontal parallel perspective angle, vertical parallel perspective angle, out-of-focus blur, and linear uniform motion blur variation images. The recognition performance of five commercial softwares on the SYSU license plate database indicates that the database is a valuable test-bed for the evaluation and analysis of license plate recognition technology.
© 2019 SPIE and IS&T 1017-9909/2019/$25.00 © 2019 SPIE and IS&T
Youting Zhao, Zhi Yu, Xiying Li, and Ming Cai "Chinese license plate image database building methodology for license plate recognition," Journal of Electronic Imaging 28(1), 013001 (5 January 2019). https://doi.org/10.1117/1.JEI.28.1.013001
Received: 11 July 2018; Accepted: 13 November 2018; Published: 5 January 2019
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