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.