  The work presents an extension of the fuzzy approach to 2-D shape recognition
[1] through refinement of initial or coarse classification decisions under a
two pass approach. In this approach, an unknown pattern is classified by
refining possible classification decisions obtained through coarse
classification of the same. To build a fuzzy model of a pattern class
horizontal and vertical fuzzy partitions on the sample images of the class are
optimized using genetic algorithm. To make coarse classification decisions
about an unknown pattern, the fuzzy representation of the pattern is compared
with models of all pattern classes through a specially designed similarity
measure. Coarse classification decisions are refined in the second pass to
obtain the final classification decision of the unknown pattern. To do so,
optimized horizontal and vertical fuzzy partitions are again created on certain
regions of the image frame, specific to each group of similar type of pattern
classes. It is observed through experiments that the technique improves the
overall recognition rate from 86.2%, in the first pass, to 90.4% after the
second pass, with 500 training samples of handwritten digits.
