  For over 50 years, researchers have been trying to teach computers to read
music notation, referred to as Optical Music Recognition (OMR). However, this
field is still difficult to access for new researchers, especially those
without a significant musical background: few introductory materials are
available, and furthermore the field has struggled with defining itself and
building a shared terminology. In this tutorial, we address these shortcomings
by (1) providing a robust definition of OMR and its relationship to related
fields, (2) analyzing how OMR inverts the music encoding process to recover the
musical notation and the musical semantics from documents, (3) proposing a
taxonomy of OMR, with most notably a novel taxonomy of applications.
Additionally, we discuss how deep learning affects modern OMR research, as
opposed to the traditional pipeline. Based on this work, the reader should be
able to attain a basic understanding of OMR: its objectives, its inherent
structure, its relationship to other fields, the state of the art, and the
research opportunities it affords.
