Computing, School of
School of Computing: Conference and Workshop Papers
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Date of this Version
2006
Document Type
Article
Abstract
Symbolic Indirect Correlation (SIC) is a nonparametric method that offers significant advantages for recognition of ordered unsegmented signals. A previously introduced formulation of SIC based on subgraph-isomorphism requires very large reference sets in the presence of noise. In this paper, we seek to address this issue by formulating SIC classification as a maximum likelihood problem. We present experimental evidence that demonstrates that this new approach is more robust for the problem of online handwriting recognition using noisy input.
Comments
Published in The 18th International Conference on Pattern Recognition (ICPR'06) Copyright © 2006 IEEE. Used by permission.