Recognition of Old Cyrillic Slavic letters: Decision tree versus fuzzy classifier experiments

In this paper we are comparing two methods for classification of Old Slavic Cyrillic characters. Traditional character recognition programs cannot be applied to the Old Slavic Church manuscripts due to the specific characteristics of these characters. The first classification method is based on a decision tree and the second one uses a fuzzy classifier. Both methods employ the same set of features extracted from the characters bitmaps. The prototypes are obtained by applying the logical operators on the samples of digitalized characters from original manuscripts. As features for defining a particular character we use number and position of spots in the outer segments, presence and position of beams and columns in horizontal and vertical segments respectively, compactness and symmetry. The fuzzy classifier creates a prototype consisting of fuzzy rules by means of fuzzy aggregation of character features. The classifier based on a decision tree is realized by a set of rules. During the creation of the classifier, several splitting measures are tested. We have created an application that implements the proposed classifiers and have experimentally tested their efficiency. Experimental results show that both classifiers correctly recognize about 50% of the characters. For 10% of the samples both classifiers make the same error and for 11% of characters the predicted character is incorrect and different.