Is. Oh et al., Analysis of class separation and combination of class-dependent features for handwriting recognition, IEEE PATT A, 21(10), 1999, pp. 1089-1094
Citations number
16
Categorie Soggetti
AI Robotics and Automatic Control
Journal title
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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.