Core Points - a framework for structural parameterization

Most implementations of single character recognition use standard arclength parameterization of the handwritten samples. A problem with the arclength approach is that points on curves of different samples from one character class may then actually correspond to parts of different structural significance. Many methods such as DTW and HMM have been successful partly because they are less sensitive to parameterizational differences. Given a sufficiently fine decomposition of a character sample into smaller segments, the complex non-linear variations of handwritten data can be viewed as a set of local linear transformations of the segments. In this paper we present a parameterization technique that implicitly defines such a structural decomposition. Experiments reveal that recognition rates for kNN template matching increase for reparameterized samples thus proving that the new parameterization removes redundance in a way that is genuinely beneficial for discrimination purposes. In addition to these quantitive results, visual inspection of the modes of singular value decomposition of reparameterized samples show that the new parameterization reduces the impact of parameterizational differences in shape variations of character samples.