Machine recognition of hand-printed Japanese characters has been an area of great interest for many years. The major problem with this classification task is the huge number of different characters. Applying standard "state-of-the- art" techniques, such as the SVM, to multi-class problems of this kind imposes severe problems, both of a conceptual and a technical nature: (i) separating one class from all others may be an unnecessarily hard problem; (ii) solving these subproblems can impose unacceptably high computational costs. In this paper, a new approach to Japanese character recognition is presented that successfully overcomes these shortcomings. It is based on a pairwise coupling procedure for probabilistic two-class kernel classifiers. Experimental results for Hiragana recognition effectively demonstrate that our method attains an excellent level of prediction accuracy while imposing very low computational costs.