Fuzzy Hough transform and an MLP with fuzzy input/output for character recognition

摘要

A near-fuzzy system for character recognition using a fuzzy Hough transform technique is present din this paper. For each character pattern, membership values are determined for a number of fuzzy sets defined on the standard Hough transform accumulator cells. These basic fuzzy sets are combined by t-norms to synthesize additional fuzzy sets whose heights form an n-dimension feature vector for the pattern. A 3n-dimensioanl fuzzy linguistic vector is generated from the n-dimension; feature vector by defining three linguistic fuzzy sets, namely, weak, moderate and strong. The linguistic set membership functions are derived from the Butter worth polynomials and are similar to the gain functions of low-pass, band-pass and high-pass fillets, respectively. A multilayer perception (MLP) is trained with the fuzzy linguistic vectors by the back propagation of errors. The MLP outputs represent fuzzy sets denoting similarity of an input feature vector to a number of character pattern classes. Recognition accuracy of the system is more than 98/100