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Although the recognition of isolated handwritten digits has been a researchtopic for many years, it continues to be of interest for the research communityand for commercial applications. We show that despite the maturity of thefield, different approaches still deliver results that vary enough to allowimprovements by using their combination. We do so by choosing fourwell-motivated state-of-the-art recognition systems for which results on thestandard MNIST benchmark are available. When comparing the errors made, weobserve that the errors made differ between all four systems, suggesting theuse of classifier combination. We then determine the error rate of ahypothetical system that combines the output of the four systems. The resultobtained in this manner is an error rate of 0.35% on the MNIST data, the bestresult published so far. We furthermore discuss the statistical significance ofthe combined result and of the results of the individual classifiers.

Comparison and Combination of State-of-the-art Techniques for Handwritten Character Recognition: Topping the MNIST Benchmark