A Maximum-Likelihood Approach to Symbolic Indirect Correlation

Symbolic indirect correlation (SIC) is a non-parametric 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