Online recognition of Chinese handwriting using a hierarchical fuzzy clustering approach

In this paper, we propose a fuzzy clustering (FC) approach to improve the template matching method described in (T. Wakahara et al., Proc. IEEE, vol.80, no.7, p.1181-1194, 1992) that demonstrated high recognition rates. Our focus is on the reduction of recognition time. The key idea is to organize the template pool in a structured manner so that matching can be performed more quickly. Through the hierarchical FC, the original template pool has the form of a decision tree where each internal node in the tree is represented by a cluster center. Since the number of class templates in each subcluster is less than the original cluster, the recognition time can be effectively reduced. An approximate formula for the number of distance calculations needed for the presented scheme is derived. The use of FC is to provide a mechanism for cluster overlapping so that in the middle of the recognition process almost 100% hit ratio can be obtained. In the experiments conducted, we obtained approximately a 3.4 times reduction of recognition time (using the simplified formula we derived) at only a 0.2% average loss of recognition rate.