Estimation of word matching accuracy in recognition of poor-quality characters

Abstract

A method is proposed for estimation of word matching accuracy in recognition of poor-quality characters. With the proposed method, all kinds of characters that might be recognized from a word are considered as networked sets, and among these sets, the sum of the occurrence probabilities is calculated for elements that cause errors in word matching. This principle makes it possible to treat all errors in word matching, which is adequate in estimation of accuracy in the case of poor-quality characters. However, results of word-level character recognition are presented as sets of candidate characters, which means an enormous number of combinations and, hence, extensive computation. In this connection, the number of combinations is materially restricted by ignoring information related to categories that are irrelevant to word matching. The proposed method makes possible an estimation of word-matching accuracy in case of frequent occurrence of poor-quality characters (rejected or misread characters), which was impossible using conventional methods. The proposed estimation method was applied to recognition of product codes, and proved efficient in accuracy estimation for poor-quality characters.