Improving the efficiency of a fuzzy-based single-stroke character recognizer with hierarchical rule-base
Alex Tormási, László Tamás Kóczy · 2012
In this paper we present an improved version of the fuzzy based single-stroke character recognizer introduced in previous works. The modified recognition method is able to reach an acceptable accuracy in the character recognition with a significant decrease on the computational complexity of the algorithm. Different hierarchical rule-base techniques were successfully used to improve the efficiency of fuzzy systems. The altered recognizer reached 98.82% average recognition rate with 26 different single-stroke symbols (based on Palm's Graffiti alphabet) without learning user-specific parameters or modifying the rule-base during the tests. The new algorithm has a small decrease in the recognition rate compared to the accuracy of the original systems but the new method has less computational price than the original system does.