Efficient Approaches for Notation Assembly in Optical Music Recognition

Abstract

Optical Music Recognition (OMR) is the field of research that studies how to computationally read music notation from written documents. Thanks to recent advances in computer vision and deep learning, there are successful approaches that are able to locate the notation elements out of a given image. However, the stage of notation assembly, in which these elements must be related to reconstructing the musical notation itself, has received little attention in the last years. Furthermore, given the large number of elements in a music score, this stage must be efficient enough to be useful in practice. In this work, a couple of neural approaches that perform this stage efficiently are studied. Our experiments using the MUSCIMA++ handwritten sheet music corpus show that there exists an underlying trade-off between effectiveness and efficiency, since each approach shows very good results in only one of these two aspects. We hope that this work represents a starting point for further research in this important stage of the OMR.