Improved handwritten digit recognition system based on fuzzy rules and prototypes created by Euclidean distance

A method is developed to classify handwritten numbers based on prototypes created using Euclidean distance, weighted voting among closest prototypes and fuzzy rules to solve confusions. The set of closest prototypes is determined by an acceptance distance. The fuzzy rules use specialized functions to measure characteristics on the handwritten digits to determine whether a digit belongs to a particular class. Classification performance is compared among following approaches: closest prototype, weighted voting including linear and exponential weighting, and voting plus fuzzy rules. The method is also compared to an algorithm based on a multilayer perceptron (MLP) network with augmented training. The best classification achieved was by voting plus fuzzy rules (96.3/spl plusmn/0.4% for 11 simulations). This result compares favorably with those obtained by MLP on the same testing database (94.6/spl plusmn/0.5%).