By Topic

Car license plate feature extraction and recognition based on multi-stage classifier

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Pu Han ; Dept. of Power Eng., North China Electr. Power Univ., Hebei, China ; Wei Han ; Dong-Feng Wang ; Yong-Jie Zhai

This paper is mainly about the recognition of car license plate characters. A method based on two-kind feature and two-stage classifier is presented. For car license plate character recognition, there are two kinds of features that can be extracted: configurable feature and statistical feature. Usually, the classifier whose inputs are statistical features is easy to train, but its robustness isn't good. The advantage of the classifier whose input is configurable feature is its better reliability, but this kind of classifier usually needs a complicated pretreatment process. So, the classifier, which based on two-kind feature and two-stage classifier, synthesizes the advantages of the two kinds of classifiers and avoids the flaws. The two classifiers in this paper are both trained by SVM. Also, the experiment results show that the recognition rate is higher, and that multi-stage classifier is obviously superior to single classifier.

Published in:

Machine Learning and Cybernetics, 2003 International Conference on  (Volume:1 )

Date of Conference:

2-5 Nov. 2003