DocumentCode :
2971247
Title :
A Fast Feature Selection Model for Online Handwriting Symbol Recognition
Author :
Huang, B.Q. ; Kechadi, M.-T.
Author_Institution :
Sch. of Comput. Sci. & Informatics, Univ. Coll. Dublin
fYear :
2006
fDate :
Dec. 2006
Firstpage :
251
Lastpage :
257
Abstract :
Many feature selection models have been proposed for online handwriting recognition. However, most of them require expensive computational overhead, or inaccurately find an improper feature set which leads to unacceptable recognition rates. This paper presents a new efficient feature selection model for handwriting symbol recognition by using an improved sequential floating search method coupled with a hybrid classifier, which is obtained by combining hidden Markov models with multilayer forward network. The effectiveness of proposed method is verified by comprehensive experiments based on UNIPEN database
Keywords :
handwriting recognition; hidden Markov models; multilayer perceptrons; pattern classification; fast feature selection model; hidden Markov models; hybrid classifier; improved sequential floating search method; multilayer forward network; online handwriting symbol recognition; Computer science; Educational institutions; Handwriting recognition; Hidden Markov models; Informatics; Multi-layer neural network; Neural networks; Search engines; Search methods; Writing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications, 2006. ICMLA '06. 5th International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7695-2735-3
Type :
conf
DOI :
10.1109/ICMLA.2006.6
Filename :
4041500
Link To Document :
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