Accelerating large character set recognition using pivots

This paper proposes a method to accelerate character recognition of a large character set by employing pivots into the search space. We divide the feature space of character categories into smaller clusters and derive the centroid of each cluster as a pivot. Given an input pattern, it is compared with all the pivots and only a limited number of clusters whose pivots have higher similarities (or smaller distances) to the input pattern are searched for with the result that we can accelerate the recognition speed. This is based on the assumption that the search space is a distance space. The method has been applied to pre-classification of a practical off-line Japanese character recognizer with the result that the pre-classification time is reduced to 61 % while keeping its pre-classification recognition rate up to 40 candidates as the same as the original 99.6% and the total recognition time is reduced to 70% of the original time without sacrificing the recognition rate at all. If we sacrifice the pre-classification rate from 99.6% to 97.7%, then its time is reduced to 35% and the total recognition time is reduced to 51.5% with recognition rate as 96.3% from 98.3%.