Adaptive combination of adaptive classifiers for handwritten character recognition

In this paper we examine the feasibility of combining two distinct layers of on-line adaptation for improving overall handwritten character recognition performance. These two approaches are adaptive classifiers and an adaptive committee used to combine them. On-line adaptive handwritten character classifiers are first discussed and the significant performance enhancements they can provide illustrated. We then examine the benefits from combining classifiers for this task, adaptive and non-adaptive, and present an adaptive committee structure suitable for this doubly adaptive framework. Experiments in combining the two adaptation approaches to form an adaptive committee consisting of adaptive member classifiers are described. The results show that while adaptation of the individual classifiers provides on average the most benefit in comparison to the non-adaptive reference level, the use of an adaptive combination of adaptive classifiers is still capable of enhancing the recognition performance by a significant margin. The usefulness of the proposed doubly adaptive approach is in this paper demonstrated in the domain of on-line handwritten character recognition, but we argue that the proposed methodology could also be applied to other application domains.