Computer Science and Engineering, Department of


Date of this Version



Published in The 18th International Conference on Pattern Recognition (ICPR'06) Copyright © 2006 IEEE. Used by permission.


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.