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In recent years, strategies based on combination of multiple classifiers have created great interest in the character recognition research community. A huge number of complex and sophisticated decision combination strategies have been explored by researchers. However, it has been realized recently that the comparatively simple Majority Voting System and its variations can achieve very robust and often comparable, if not better, performance than many of these complex systems. In this paper, a review of various Majority Voting Systems and their variations are discussed, and a comparative study of some of these methods is presented for a typical character recognition task.

Multiple Classifier Combination for Character Recognition: Revisiting the Majority Voting System and Its Variations