Pattern Recognition, International Conference on (2002)
Quebec City, QC, Canada
Aug. 11, 2002 to Aug. 15, 2002
Charles Mathis , Xerox PARC
Thomas Breuel , Xerox PARC
A key problem faced by classifier s is coping with styles not represented in the training set. We present an application of hierarchical Bayesian methods to the problem of recognizing degraded printed characters in a variety of fonts. The proposed method works by using training data of various styles and classes to compute prior distributions on the parameters for the class conditional distributions. For classification, the parameters for the actual class conditional distributions are fitted using an EM algorithm. The advantage of hierarchical Bayesian methods is motivated with a theoretical example. Severalfold increases in classification performance relative to style-oblivious and style-conscious are demonstrated on a multifont OCR task.
T. Breuel and C. Mathis, "Classification Using a Hierarchical Bayesian Approach," Pattern Recognition, International Conference on(ICPR), Quebec City, QC, Canada, 2002, pp. 40103.