Applying decision trees to the recognition of musical symbols

The paper presents an experimental study on the recognition of printed musical scores. The first part of the study focuses on data preparation. Bitmaps containing musical symbols are converted to feature vectors using various methods. The vectors created in such a way are used to train classifiers which are the essential part of the study. Several decision tree classifiers are applied to this recognition task. These classifiers are created using different decision tree induction methods. The algorithms incorporate different criteria to select attributes in the nodes of the trees. Moreover, some of them apply stopping criteria, whereas the others perform tree pruning. The classification accuracy of the decision trees is estimated on data taken from musical scores. Eventually the usefulness of decision trees in the recognition of printed musical symbols is evaluated.