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Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04)
Generative Models and Bayesian Model Comparison for Shape Recognition
Kokubunji, Tokyo, Japan
October 26-October 29
ISBN: 0-7695-2187-8
Balaji Krishnapuram, Microsoft Research
Christopher M. Bishop, Microsoft Research
Martin Szummer, Microsoft Research
Recognition of hand-drawn shapes is an important and widely studied problem. By adopting a generative probabilistic framework we are able to formulate a robust and flexible approach to shape recognition which allows for a wide range of shapes and which can recognize new shapes from a single exemplar. It also provides meaningful probabilistic measures of model score which can be used as part of a larger probabilistic framework for interpreting a page of ink. We also show how Bayesian model comparison allows the trade-off between data fit and model complexity to be optimized automatically.
Citation:
Balaji Krishnapuram, Christopher M. Bishop, Martin Szummer, "Generative Models and Bayesian Model Comparison for Shape Recognition," iwfhr, pp.20-25, Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04), 2004
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