  Although the recognition of isolated handwritten digits has been a research
topic for many years, it continues to be of interest for the research community
and for commercial applications. We show that despite the maturity of the
field, different approaches still deliver results that vary enough to allow
improvements by using their combination. We do so by choosing four
well-motivated state-of-the-art recognition systems for which results on the
standard MNIST benchmark are available. When comparing the errors made, we
observe that the errors made differ between all four systems, suggesting the
use of classifier combination. We then determine the error rate of a
hypothetical system that combines the output of the four systems. The result
obtained in this manner is an error rate of 0.35% on the MNIST data, the best
result published so far. We furthermore discuss the statistical significance of
the combined result and of the results of the individual classifiers.
