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

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