In this paper we present two modified and improved versions of the formerly published Fuzzy-Based SingleStroke Character Recognizer (FUBAR) algorithm. After introducing the original method, the study investigates the effects of two different improvements of the designed algorithm. The first extension is the use of symbol-dependent fuzzy grids to extract symbol features; the second one is the use of rule weights in hierarchical rule-bases. The accuracy and efficiency of the extended FUBAR algorithms are compared to previous results.
In this paper the results of rule-base construction parameter optimization for a multi-stroke fuzzy character recognizer are compared. The experiment covers the investigation of the optimal number of samples used to build the rule-base and the parameter of the method to generate fuzzy sets from the training set collected from subjects. The various settings are evaluated with validation samples from the same group of test subjects.
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