Normalization ensemble for handwritten character recognition

This paper proposes a multiple classifier approach, called normalization ensemble, for handwritten character recognition by combining multiple normalization methods. By varying the coordinate mapping mode, we have devised 14 normalization functions, and switching on/off slant correction results in 28 instantiated classifiers. We will show that the classifiers with different normalization methods are complementary and the combination of them can significantly improve the recognition accuracy. In experiments of handwritten digit recognition on the NIST Special Database 19, the normalization ensemble was shown to reduce the error rate by factors from 10.6% to 26.9% and achieved the best error rate 0.43%. We also show that the complexity of normalization ensemble can be reduced by selecting seven classifiers from 28 with little loss of accuracy.