Learning Spline Models with the EM Algorithm for Shape Recognition

Abstract

This paper demonstrates how cubic Spline (B-Spline) models can be used to recognize 2-dimension nonrigid handwritten isolated characters. Each handwritten character is represented by a set of nonoverlapping uniformly distributed landmarks. The Spline models are constructed by utilizing cubic order of polynomial to model the shapes under study. The approach is a two-stage process. The first stage is learning, we construct a mixture of spline class parameters to capture the variations in spline coefficients using the apparatus Expectation Maximization algorithm. The second stage is recognition, here we use the Fréchet distance to compute the variations between the spline models and test spline shape for recognition. We test the approach on a set of handwritten Arabic letters