Abstract
In the context of word recognition, for a word set with high similarity between words, when using word matching based on key characters, a method is proposed for optimal selection of key characters so as to reduce the average number of candidate words while ensuring a high probability of success in the search for the right word. With the proposed method, the success probability of the word search and the average number of candidate words are calculated for all combinations of key characters, and the optimal combination is then selected. To implement this principle, a new method was developed to calculate the average number of candidate words. To make the proposed principle practicable in terms of computation requirements, a method for seeking an approximate solution using genetic algorithms was proposed. The validity of the proposed method was confirmed experimentally in the recognition of personal names written with katakana characters. © 1998 Scripta Technica. Syst Comp Jpn, 28(14): 33–40, 1997
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