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Subject: search.filters.subject.Character Recognition

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    Experimental results of using rough sets for printed Thai characters recognition
    (2001-12-01)
    Mitatha, S.
    ;
    Dejharn, K.
    ;
    Chevasuvit, F.
    ;
    Chankuang, B.
    ;
    Kasemsiri, W.
    This paper proposes the experimental results of using a Rough sets for the recognition of printed Thai characters. In our experiment, we segment each character into 32 pieces sized 4×4 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 set 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 apply the set of rule to the unknown which related to each set of rules's training sets are 100% for all three set of rules.
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    Printed thai character recognition using Fuzzy-Rough Sets
    (2001-12-01)
    Kasemsiri, Watjanapong
    ;
    Kimpan, Chom
    This paper proposes the method of using the Fuzzy-Rough Sets for the recognition of Thai Characters. In our work we divide the classification process into 2 levels, coarse and fine classification. Both levels of classification have the same processes, applying Rough Set's lower approximation and then using Fuzzy-Rough sets. The different point between those two levels is the features of inputted data used for classifying. There are 40 coarse groups and some of them need not to pass through the second level of classification. We trained this system with 2816 training samples, which composed of 4 fonts and 4 sizes characters. The system is tested with unknown sample, which composed of 7 fonts and 7 sizes characters, 4 fonts and 4 sizes of the training sample are inclusive. The accuracy of this proposed system is as high as 89%.