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

Daniel Keysers

techreport DFKI 5/2006.


Although the recognition of isolated handwritten digits has been a re- search 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 be- tween 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.

DkComp+CombCharRec.pdf (pdf, 551 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence