CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2005 vol.27 Issue No.01 - January
Issue No.01 - January (2005 vol.27)
Prateek Sarkar , IEEE Computer Society
George Nagy , IEEE
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.18
In many applications of pattern recognition, patterns appear together in groups (fields) that have a common origin. For example, a printed word is usually a field of character patterns printed in the same font. A common origin induces consistency of style in features measured on patterns. The features of patterns co-occurring in a field are statistically dependent because they share the same, albeit unknown, style. Style constrained classifiers achieve higher classification accuracy by modeling such dependence among patterns in a field. Effects of style consistency on the distributions of field-features (concatenation of pattern features) can be 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. Based on this model, an optimal style constrained classifier processes entire fields of patterns rendered in a consistent but unknown style. In a laboratory experiment, style constrained classification reduced errors on fields of printed digits by nearly 25 percent over singlet classifiers. Longer fields favor our classification method because they furnish more information about the underlying style.
Style, isogenous patterns, style consistency, style constrained classification, style-bound variant, style-shared variant, Optical Character Recognition, font recognition, field classification, mixture model.
Prateek Sarkar, George Nagy, "Style Consistent Classification of Isogenous Patterns", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.27, no. 1, pp. 88-98, January 2005, doi:10.1109/TPAMI.2005.18