Using association rules to improve Chinese handwritten character recognition

Association rules are useful for determining correlation between attributes of a relation and have applications in marketing, financial and retail sectors. In this paper, we present an approach for combining handwritten character classifiers based on association rules, which reflect the correlation between the classifiers. The experimental results show that the association rules improve the performances of the integrated system significantly. An experimental comparison of two combination schemes is also provided