Experiment on Character Recognition Using Fuzzy Filters
The theory of fuzzy set first introduced by Lofti Zadeh is employed in designing filters. The filters are used in the automatic recognition of printed alphanumeric character patterns by comparing a dictionary of 37 standard masks to the noisy data pattern and selecting the mask which fits best. The basic building blocks of our proposed fuzzy filters consists of Boolean and fuzzy logic operations, Boolean and fuzzy relational operations, and mixed functions of fuzzy variables. Extensive computer simulation was performed to evaluate the performance. The results are very encouraging for both versions of our design; (i) nearly perfect recognition rate was achieved with up to 22.86% of picture cells corrupted by noise, and (ii) better than 90% correct recognition rate was observed with up to 31.43% (Filter I) and 37.14% (Filter II) of picture cells contaminated by noise. We believe that fuzzy set theory will be a very powerful tool in filter design, providing simplicity in implementation of both hardware and software.
KeywordsMembership Function Character Recognition Fuzzy Variable Fuzzy Graph Alphanumeric Character
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