A system for licence plate recognition using a hierarchically combined classifier

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dc.contributor.author Zheng, Lihong en_US
dc.contributor.author Wu, Qiang en_US
dc.contributor.author Samali, Bijan en_US
dc.contributor.author He, Sean en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-10-12T03:33:45Z
dc.date.available 2012-10-12T03:33:45Z
dc.date.issued 2011 en_US
dc.identifier 2011002114 en_US
dc.identifier.citation Zheng Lihong et al. 2011, 'A system for licence plate recognition using a hierarchically combined classifier', Inderscience Enterprises Limited, vol. 10, pp. 189-202. en_US
dc.identifier.issn 1740-8873 en_US
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/18264
dc.description.abstract In a real time, automatic licence plate recognition system, licence detection, character segmentation and character recognition are three important components. All these three components generally require high accuracy and fast recognition speed to process. In this paper, general processing steps for license plate recognition (LPR) are addressed. After three types of combined classifiers are introduced and compared, a hierarchically combined classifier is designed based on an inductive learning-based method and an support vector machine (SVM)-based classification. This approach employs the inductive learning-based method to roughly divide all classes into smaller groups. Then, the SVM approach is used for character classification in individual groups. Having obtained a collection of samples of characters in advance from licence plates after licence detection and character segmentation steps, some known samples are available for training. After the training process, the inductive learning rules are extracted for rough classification and the parameters used for SVM-based classification are obtained. Then, a classification tree is constructed for next fast training and testing processes based on SVMs. The experimental results show that the hierarchically combined classifier is better than either the inductive learning-based classification or the SVM-based classification with a lower error rate and a faster processing speed. en_US
dc.language en_US
dc.publisher Inderscience Enterprises Limited en_US
dc.relation.isbasedon en_US
dc.relation.isbasedon http://dx.doi.org/10.1504/IJISTA.2011.039019 en_US
dc.title A system for licence plate recognition using a hierarchically combined classifier en_US
dc.parent International Journal of Intelligent Systems Technologies and Applications en_US
dc.journal.volume 10 en_US
dc.journal.number en_US
dc.publocation UK en_US
dc.identifier.startpage 189 en_US
dc.identifier.endpage 202 en_US
dc.cauo.name FEIT.School of Computing and Communications en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 0000074100 en_US
dc.personcode 990421 en_US
dc.personcode 000748 en_US
dc.personcode 870186 en_US
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords licence plate recognition; class tree; hierarchically combined classifier. en_US
dc.staffid en_US
dc.staffid 870186 en_US

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