Nayer Wanas, Mahmoud R. El-Sakka, and Mohamed S. Kamel, "Multiple Classifier Hierarchical Architecture for Handwritten Arabic Character Recognition", International Joint Conference on Neural Networks, IJCNN'1999, Vol. 4, pp. 2834-2838, July 1999, Washington DC, USA.
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. In this work, we present a new architecture that integrates learning into the voting scheme used to aggregate individual decisions. The focus of this work is to make the decision fusion a more adaptive process. This approach makes use of feature detectors responsible of 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.