Automatic Knowledge Acquisition: Recognizing Music Notation with Methods of Centroids and Classifications Trees

This paper presents a pattern recognition study aimed al music symbols recognition. The study is focused on classification methods of music symbols based on decision trees and clustering method applied to classes of music symbols that face classification problems. Classification is made on the basis of extracted features. A comparison of selected classifiers was made on some classes of nutation symbols distorted by a variety of factors as image noise, printing defects, different fonts, skew and curvature of scanning, overlapped symbols.