The success gained by applying bit-plane decomposition methods to handwriting recognition have been demonstrated in our previous work [S. Hoque et al., 2003, 2002]. In this paper we address the relationship between the diversity and the improvements obtained by applying multiple combinations of various layers. These layers are obtained by applying a method based on an n-tuple based classification system, namely, the random decomposition technique proposed in S. Hoque et al. (2003). We investigated 5 combination methods and 9 diversity measures using data extracted from the NIST database. Results presented in this paper support the use of the bit-plane decomposition approach as a diversification method. Strong correlation was found between both the accuracy and the improvements and the diversity measures in the majority of the combination methods investigated.
['Samuel Chindaro', 'Konstantinos Sirlantzis', 'Michael C. Fairhurst', 'Sanaul Hoque']
Diversity-performance relationship in a handwriting recognition system based on bit-plane decomposition