Structuring Search Space for Accelerating Large Set Character Recognition

Yiping YANG  Bilan ZHU  Masaki NAKAGAWA  

IEICE TRANSACTIONS on Information and Systems   Vol.E88-D   No.8   pp.1799-1806
Publication Date: 2005/08/01
Online ISSN: 
DOI: 10.1093/ietisy/e88-d.8.1799
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Document Image Understanding and Digital Documents)
Category: Search Space for Character Recognition
character recognition,  large character set,  search space,  pivot,  candidate selection,  

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This paper proposes a "structuring search space" (SSS) method aimed to accelerate recognition of large character sets. 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 similarity (or smaller distance) to the input pattern are searched in, thus accelerating the recognition speed. This is based on the assumption that the search space is a distance space. We also consider two ways of candidate selection and finally combine them the method has been applied to a practical off-line Japanese character recognizer with the result that the coarse classification time is reduced to 56% and the whole recognition time is reduced to 52% while keeping its recognition rate as the original.