Optical Music Recognition (OMR) aims to recognize and understand written music scores. With the help of Deep Learning, researchers were able to significantly improve the state-of-the-art in this research area. However, Deep Learning requires a substantial amount of annotated data for supervised training. Various datasets have been collected in the past, but without a common standard that defines data formats and terminology, combining them is a challenging task. In this paper we present our approach towards unifying multiple datasets into the largest currently available body of over 90000 musical symbols that belong to 79 classes, containing both handwritten and printed music symbols. A universal music symbol classifier, trained on such a dataset using Deep Learning, can achieve an accuracy that exceeds 98%.
A. Pacha, H. Eidenberger: "Towards a Universal Music Symbol Classifier"; Talk: International Workshop on Graphics Recognition, New York; 11-09-2017 - 11-10-2017; in: "Proceedings of the 12th International Workshop on Graphics Recognition", (2017), ISBN: 978-1-5386-3586-5; 2 pages.
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