By Topic

The research for license plate recognition using sub-image fast independent component analysis

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
$33 $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

3 Author(s)
Jian W. Fang ; School of Electronic and Control Engineering, Chang'an University, Xi'an, 710064, PRC ; Wei S. Yang ; Hong K. Xu

In order to solve the problem that current license plate recognition methods, such as template matching and neural network computing, which need a large number of samples and large amount of computation, this paper proposed a sub-image fast independent component analysis (SI-FastICA) method for plate recognition. It can obtain the local feature of the image with a small amount of computation. In order to obtain better recognition results, in the stage of character segmentation, this paper carried segmentation based on the proposed relative coordinate dichotomy. Then, the feature of characters was extracted by SI-FastICA. The experiments show that SI-FastICA can reflect the local characteristics of the character very well. At last, this paper put the collected actual license plate images into experiment, and achieved good recognition results.

Published in:

2011 Chinese Control and Decision Conference (CCDC)

Date of Conference:

23-25 May 2011