Recognition using only visual evidence cannot always be successful due to limitations of information and resources available during training. Considering relation among lexicon entries is sometimes useful for decision making. In this paper we present a method to capture lexical similarity of a lexicon and reliability of a character recognizer which serve to capture the dynamism of the environment. A parameter, lexical similarity, is defined by measuring these two factors as edit distance between lexicon entries and separability of each character's recognition results. Our experiments show that a utility function considering lexical similarity in a decision stage can enhance the performance of a conventional word recognizer.