A hierarchically combined classifier for license plate recognition

Publication Type:
Conference Proceeding
Citation:
Proceedings - 2008 IEEE 8th International Conference on Computer and Information Technology, CIT 2008, 2008, pp. 372 - 377
Issue Date:
2008-09-22
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2008001712.pdf339.28 kB
Adobe PDF
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 SVMbased 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 SVMbased classification in terms of error rates and processing speeds. © 2008 IEEE.
Please use this identifier to cite or link to this item: