Top-Down Likelihood Word Image Generation Model for Holistic Word Recognition
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Classifier Adaptation with Non-representative Training Data
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Towards a Ptolemaic Model for OCR
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Style Context with Second-Order Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Style Consistent Classification of Isogenous Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analytical Results on Style-Constrained Bayesian Classification of Pattern Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Abstract: In many applications of pattern recognition, patterns appear in groups (fields) that have a common origin. For example, a printed word is a field 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 a field increases 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.