Using Class Separation for Feature Analysis and Combination Features of Class-dependent

In tkis paper, we analyze the class separation ofthefeatures in handwriting recognition. Behaviors of measurement tools are studied with partial and full class$cations. A new scheme ofselecting and combining class-dependentfeatures is proposed. In this scheme, a class is considered to have its own optimalfeature vectorfOr discriminating irselfSrom tke other classes. Using an architecture of modular neural nehvorks as tke classifier, a series of experiments kave been conducted on totally unconstrained handwritten numerals. The results indicate that the selectedfeatures are efective in separating pattern classes and the new feature vector derived from a combination of two types of suck featuresfurther improves the recognition rate.