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A hierarchically combined classifier for license plate recognition

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6 Author(s)
Lihong Zheng ; Faculty of Information Technology, University of Technology, Sydney, PO Box 123, Broadway NSW 2007 AUSTRALIA ; Xiangjian He ; Qiang Wu ; Wenjing Jia
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High accuracy and fast recognition speed are two requirements for real-time and automatic license plate recognition system. In this paper, we propose a hierarchically combined classifier based on an inductive learning based method and an SVM-based classification. This approach employs the inductive learning based method to roughly divide all classes into smaller groups. Then the SVM method is used for character classification in individual groups. Both start from a collection of samples of characters from license plates. After a training process using some known samples in advance, 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 further fast training and testing processes for SVM-based classification. Experimental results for the proposed approach are given. From the experimental results, we can make the conclusion that the hierarchically combined classifier is better than either the inductive learning based classification or the SVM-based classification in terms of error rates and processing speeds.

Published in:

Computer and Information Technology, 2008. CIT 2008. 8th IEEE International Conference on

Date of Conference:

8-11 July 2008