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The classification of musical symbols is an important step for Optical Music Recognition systems. However, little progress has been made so far in the recognition of handwritten notation. This paper considers a scheme that combines ideas from ensemble classifiers and dissimilarity space to improve the classification of handwritten musical symbols. Several sets of features are extracted from the input. Instead of combining them, each set of features is used to train a weak classifier that gives a confidence for each possible category of the task based on distance-based probability estimation. These confidences are not combined directly but used to build a new set of features called Confidence Matrix, which eventually feeds a final classifier. Our work demonstrates that using this set of features as input to the classifiers significantly improves the classification results of handwritten music symbols with respect to other features directly retrieved from the image.

Recognition of Handwritten Music Symbols using Meta-features Obtained from Weak Classifiers based on Nearest Neighbor