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Multi-prototype classification: Improved modelling of the variability of handwritten data using statistical clustering algorithms

By Ahmad Fuad Rezaur Rahman and Michael Fairhurst


The principal obstacle in successfully recognising handwritten data is thr inherent degree of intra-class variability encountered, This calls for subclass modelling of handwritten data based on the statistically significant variations within the main classes. A novel multi-prototyping approach based on statistical clustering techniques is investigated as an appropriate solution to this problem and very encouraging results have been achieved

Topics: TK
Year: 1997
DOI identifier: 10.1049/el:19970848
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