A Hierarchically Combined Classifier for License Plate Recognition

Lihong Zheng, Xiangjian He, Qiang Wu, Wenjing Jia, B. Samali, M. Palaniswami

Research output: Book chapter/Published conference paperConference paperpeer-review

2 Citations (Scopus)
19 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationIEEE International Conference on Computer and Information Technology
Place of PublicationUSA/Australia
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781424423576
Publication statusPublished - 2008
EventICCIT 2008: 8th International Conference - Sydney, NSW Australia, Australia
Duration: 08 Jul 200811 Jul 2008


ConferenceICCIT 2008: 8th International Conference


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