Multiple classifier hierarchical architecture for handwritten Arabic character recognition

Combining decisions from several classifiers can be used to improve on the results of handwritten characters recognition. There are different methods to combine these decisions, most of which are static. We present an architecture that integrates learning into the voting scheme used to aggregate individual decisions. The focus of the work is to make the decision fusion a more adaptive process. This approach makes use of feature detectors responsible for gathering information about the input to perform adaptive decision aggregation. The approach is tested on handwritten Arabic character recognition. The results showed an improvement over any individual classifier, as well as different static classifier combining schemes