A Coarse Classifier Construction Method from a Large Number of Basic Recognizers for On-line Recognition of Handwritten Japanese Characters

This paper describes a method for constructing the most efficient and robust coarse classifier from a large number of basic recognizers which are obtained by different parameters of feature extraction, different discriminant methods or functions, and so on. The architecture of the coarse classification is a sequential cascade of basic recognizers and reduces the candidates after each basic recognizer. Genetic algorithm determines the best cascade with the best speed and highest performance. The method is applied for on-line handwritten Japanese characters recognition. We produced 201 basic recognizers of MQDF, 21 basic recognizers of Euclidian distance and 21 basic recognizers of the LSS method by changing parameters. From these basic recognizers we have obtained a rather simple 2 stages cascade with the result that the whole recognition time was reduced to 24.5% while keeping classification and recognition rates.