Optimizing Parameters in the Layered Search Space

This paper reports optimization efforts on a layered search space method aimed at accelerating the recognition of a large prototype set. The layered search space method classifies similar prototypes into clusters. Representative prototypes, each for one of these clusters, are then selected and further classified into higher-level clusters, and so on. In finding a prototype, we first identify the highest-level clusters where the prototype may be found, then proceed to identify the most likely subclusters within these clusters, and so on. Finally, we match the input with the prototypes in the identified lowest level clusters. Increasing layers will decrease the number of prototypes to be matched, but the precision of candidate selection will decrease and overhead will increase. Hence there are several parameters that one needs to adjust for the method to perform optimally. Most importantly, there is an optimal number of layers that accelerates the recognition without compromising the recognition rate. We used two efficient methods to approximately identify this number. Both the methods show that having two layers achieves this optimality for the recognition of handwritten Japanese characters.