Handwritten Music Symbol Classification Using Deep Convolutional Neural Networks

Abstract

In this paper, we utilize deep Convolutional Neural Networks (CNNs) to classify handwritten music symbols in HOMUS data set. HOMUS data set is made up of various types of strokes which contain time information and it is expected that online techniques are more appropriate for classification. However, experimental results show that CNN which does not use time information achieved classification accuracy around 94.6% which is way higher than 82% of dynamic time warping (DTW), the prior state-of-the-art online technique. Finally, we achieved the best accuracy around 95.6% with the ensemble of CNNs.