Analysis of class separation and combination of class-dependent features for handwriting recognition

In this paper, we propose a new approach to combine multiple features in ha ndwriting recognition based on two ideas: feature selection-based combinati on and class-dependent features. A nonparametric method is used for feature evaluation, and the first part of this paper is devoted to the evaluation of features in terms of their class separation and recognition capabilities . In the second part, multiple feature vectors are combined to produce a ne w feature vector. Based on the fact that a feature has different discrimina ting powers for different classes, 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 clas sifier, a series of experiments were conducted on unconstrained handwritten numerals. The results indicate that the selected features are effective in separating pattern classes and the new feature vector derived from a combi nation of two types of such features further improves the recognition rate.