Improving the structuring search space method for accelerating large set character recognition

This paper proposes enhancement of the "structuring search space " (SSS) method attempted in [Y. Yang, et al., (2003)] to further accelerate the recognition speed. It consists of structuring the search space into two layers, improving the candidate selection algorithm and selecting candidates depending on the top candidate. For two-layered search space, we divide all of the prototypes into smaller clusters and derive the centroid of each cluster as a pivot, then again cluster all of the pivots and derive the centroid of each cluster (super cluster) as a super pivot. An input pattern is compared with all the super pivots and several super clusters are selected whose super pivots are close to the input pattern. Then, the input pattern is compared with pivots in the selected super clusters, close pivots are selected and prototypes within the clusters of the selected pivots are treated as candidates for fine classification. Thus, the number of prototypes compared with the input pattern is greatly reduced. Moreover, we employ a synthetic candidate selection algorithm and a top candidate dependent candidate selection method. Since the top candidate suggests where the input pattern is mapped in the feature space, it can provide the information on how candidates should be selected in coarse classification. Thus, this information is specified in each prototype for the case when it is selected as the top candidate and specified values are employed for selecting a variable number of candidates. These improvements have been incorporated into a practical off-line Japanese character recognizer consisting of coarse classification and fine classification with the result that the coarse classification time is reduced to 28.6% and the whole recognition time is reduced to 31.3% from the original time while sacrificing a very limited recognition rate (98. 1 % to 97.7%).