Style-consistency in isogenous patterns

In many applications of pattern recognition, patterns appear in groups (fields) that have a common origin. For example, a printed word is afield of character patterns printed in the same font. A common origin induces consistency of style among features measured on patterns. In the presence of multiple styles, the features of co-occurring patterns are statistically dependent through the underlying style. Modeling such dependence among constituent patterns of afield increases the classification accuracy. Effects of style consistency on the distributions of field features (concatenation of pattern features) are modeled by hierarchical mixtures. Each field derives from a mixture of styles, while, within a field, a pattern derives from a class-style conditional mixture of Gaussians. An optimal (least-error) style-conscious classifier processes entire fields of patterns rendered in a consistent but unknown style, based on the model. In a laboratory experiment, style-conscious classification reduced errors on fields of printed digits by nearly 25% over singlet classifiers. Longer fields favor our classification method, because they furnish more information about the underlying style.