Using class separation for feature analysis and combination of class-dependent features

We analyze the class separation of the features in handwriting recognition. Behaviors of measurement tools are studied with a partial and full classifications. A new scheme of selecting and combining class-dependent features is proposed. In this scheme, a class is considered to have its own optimal feature vector for discriminating itself from the other classes. Using an architecture of modular neural networks as the classifier, a series of experiments have been conducted on totally unconstrained handwritten numerals. The results indicate that the selected features are effective in separating pattern classes and the new feature vector derived from a combination of two types of such features further improves the recognition rate