CASCADING MULTIPLE CLASSIFIERS AND REPRESENTATIONS FOR OPTICAL AND PEN-BASED HANDWRITTEN DIGIT RECOGNITION

We discuss a multistage method, cascading, where there is a sequence of classifiers ordered in terms of complexity (of the classifier or the repre- sentation) and specificity, in that early classifiers are simple and general and later ones are more complex and are local. For building portable, low-cost handwriting recognizers, memory and computational requirements are as critical as accuracy and our proposed method, cascading, is a way to gain from having multiple classifiers, without much losing from cost. Simulation results on optical and pen-based handwriting digit recognition indicate that when compared with voting, mixture of experts and stacking, our proposed method, cascading, does stand out as the most realistic combination scheme