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

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2 Author(s)
Rahman, A.F.R. ; Electron. Eng. Labs., Kent Univ., Canterbury, UK ; Fairhurst, M.C.

The principal obstacle in successfully recognising handwritten data is the 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

Published in:

Electronics Letters  (Volume:33 ,  Issue: 14 )