Article ID Journal Published Year Pages File Type
530909 Pattern Recognition 2014 9 Pages PDF
Abstract

•We propose a method for coarse classifier construction.•It constructs a coarse classifier from 243 basic recognizers.•The basic recognizers are obtained by different parameters.•The architecture of the coarse classifier is a sequential cascade of basic recognizers.•A genetic algorithm determines the best cascade.

In this paper, a systematic method is described that constructs an efficient and a robust coarse classifier from a large number of basic recognizers obtained by different parameters of feature extraction, different discriminant methods or functions, etc. The architecture of the coarse classification is a sequential cascade of basic recognizers that reduces the candidates after each basic recognizer. A genetic algorithm determines the best cascade with the best speed and highest performance. The method was applied for on-line handwritten Chinese and Japanese character recognitions. We produced hundreds of basic recognizers with different classification costs and different classification accuracies by changing parameters of feature extraction and discriminant functions. From these basic recognizers, we obtained a rather simple two-stage cascade, resulting in the whole recognition time being reduced largely while maintaining classification and recognition rates.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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