Comparison of Models for Recognition of Old Slavic Letters

Springer papers

Authors

Mimoza Klekovska , Cveta Martinovska , Igor Nedelkovski and Dragan Kaevski

Abstract

This paper compares two methods for classification of Old Slavic letters. Traditional letter recognition programs cannot be applied on the Old Slavic Cyrillic manuscripts because these letters have unique characteristics. The first classification method is based on a decision tree and the second one uses fuzzy techniques. Both methods use the same set of features extracted from the letter bitmaps. Results from the conducted research reveal that discriminative features for recognition of Church Slavic Letters are number and position of spots in the outer segments, presence and position of vertical and horizontal lines, compactness and symmetry. The efficiency of the implemented classifiers is tested experimentally.

Keywords

Handwritten Letter Recognition Fuzzy Decision Decision Tree Feature Extraction Recognition Accuracy Precision