Pattern recognition/classification is increasingly drawing the attention of scientific research
because of its important roll in automation and human-machine communication. Even
though many models have been introduced to deal with classification, because of the
inherited imprecision and ambiguity, these models did not tackle the problem in an
efficient way. Traditional models deal only with statistical uncertainty (randomness) but
not with the non-statistical uncertainty (vagueness). Fuzzy set theory allows us to better
understand imprecision in both of its categories: vagueness and randomness. The
incorporation of fuzzy set theory in existing algorithms helped in many cases to improve
the performance and increase the efficiency of those algorithms.
This thesis will explore fuzzy logic as it pertains to pattern recognition. In order to
demonstrate fuzzy logic, the problem of recognizing the Arabic alphabet is discussed. In
this problem moments and central moments were used as discriminating features.
A fuzzy classifier was designed in a way that incorporated some statistical knowledge of
the problem in hand. Performance of this classifier was compared to a Bayesian classifier
and a neural network classifier. Performance, evaluation...