A Maximum-Likelihood Approach to Symbolic Indirect Correlation

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.

• Type: Conference
• Year: 2006
• Where: ICPR
• Authors: Ashutosh Joshi, Daniel P. Lopresti, George Nagy, Sharad C. Seth
• Keywords: Computer Vision, ICPR 2006, Maximum Likelihood Problem, SIC Classification, Symbolic Indirect Correlation