By Topic

Combination of multiple classifiers for Chinese recognition

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)
Te-Wei Chiang ; Dept. of Accounting Inf. Syst., Chihlee Inst. of Technol., Taipei, Taiwan ; Mann-Jung Hsiao ; Tien-Wei Tsai

Traditional character recognition systems use a single classifier to determine the true class of a given character. However, by using classifiers of different types simultaneously, classification accuracy could be improved. In this paper, we propose a new approach based on majority vote and statistics to support a combined decision among multiple classifiers. First, we find the strengths and weaknesses of all classifiers through the analysis among test characters, templates and classifiers. Then we devise a combination method that can improve classification performance. Experimental results show the effectiveness of our approach.

Published in:

Networking, Sensing and Control, 2004 IEEE International Conference on  (Volume:2 )

Date of Conference: