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Experimental results of using rough sets for printed Thai characters recognition

This paper proposes the experimental results of using rough sets for the recognition of printed Thai characters. We segment each character into 32 pieces sized 4/spl times/4 pixels, and then find the distribution of pixels that match a value of "1" (black dot) in each section. Following this, we use the resulting 32 values as the attributes for each given object. Afterwards, we create 3 sets of decision making rules from 3 different training sets and use those 3 sets of rules to classify each member of the unknown set. This set is composed of 42 Thai characters, excluding the two that are very rarely used, with 7 fonts and 7 sizes, for a total of 2058. The results are 46.20%, 63.15%, 73.12% for the first set of rules, the second set of rules and the third set of rules respectively. And the results when applying the set of rules to the unknowns related to each set of rules' training sets are 100% for all three set of rules.