Title: Learning-based license plate detection in vehicle image database

Authors: Huaifeng Zhang, Wenjing Jia, Xiangjian He, Qiang Wu

Addresses: Faculty of Information Technology, University of Technology, Sydney, P.O. Box 123, Broadway, NSW, 2007, Australia. ' Faculty of Information Technology, University of Technology, Sydney, P.O. Box 123, Broadway, NSW, 2007, Australia. ' Faculty of Information Technology, University of Technology, Sydney, P.O. Box 123, Broadway, NSW, 2007, Australia. ' Faculty of Information Technology, University of Technology, Sydney, P.O. Box 123, Broadway, NSW, 2007, Australia

Abstract: This paper proposes a learning-based algorithm to detect license plates of vehicles from vehicle image database. There are three main contributions in this paper. The first contribution is to define a novel vertical edge map, which makes the image processing more effectively. The second contribution is to propose a learning-based cascade classifier composing of two kinds of sub-classifiers, which makes the system very robust. The third contribution is to experimentally estimate the parameter of scaling factor and chose an optimal one for the algorithm to seek a good balance between detection rate and processing time.

Keywords: content-based image retrieval; CBIR; vehicle image databases; license plate detection; AdaBoost; cascade classifiers; vehicle license plates; learning algorithms; image processing.

DOI: 10.1504/IJIIDS.2007.014952

International Journal of Intelligent Information and Database Systems, 2007 Vol.1 No.2, pp.228 - 243

Published online: 20 Aug 2007 *

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