DocumentCode :
2337424
Title :
HMM-based on-line multi-stroke sketch recognition
Author :
Jiang, Wei ; SUN, ZHENG-XING
Author_Institution :
State Key Lab. for Novel Software Technol., Nanjing Univ., China
Volume :
7
fYear :
2005
fDate :
18-21 Aug. 2005
Firstpage :
4564
Abstract :
This paper describes a new approach for on-line multi-stroke sketch recognition. The approach is based on hidden Markov model (HMM). Sketches are modeled to HMM chains, and strokes are mapped to different HMM states. The proposed approach introduces a new method to determine HMM state-number, based on which an adaptive HMM sketch recognizer is constructed. A combined feature based on curvature, velocity and geometrical character of stroke for sketch recognition is also proposed to improve recognition accuracy. Finally, the experiments prove the effectiveness and efficiency of the proposed approach.
Keywords :
character recognition; feature extraction; hidden Markov models; image recognition; HMM state-number; Markov chains; adaptive hidden Markov model sketch recognition; feature extraction; geometrical character; online multistroke sketch recognition; sketch modeling; stroke curvature; stroke mapping; stroke velocity; Character recognition; Engines; Handwriting recognition; Hidden Markov models; Shape; Speech recognition; Sun; Support vector machine classification; Support vector machines; Topology; Adaptive Hidden Markov model; Multi-stroke; Sketch Recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
Type :
conf
DOI :
10.1109/ICMLC.2005.1527743
Filename :
1527743
Link To Document :
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