  Optical Music Recognition (OMR) is concerned with transcribing sheet music
into a machine-readable format. The transcribed copy should allow musicians to
compose, play and edit music by taking a picture of a music sheet. Complete
transcription of sheet music would also enable more efficient archival. OMR
facilitates examining sheet music statistically or searching for patterns of
notations, thus helping use cases in digital musicology too. Recently, there
has been a shift in OMR from using conventional computer vision techniques
towards a deep learning approach. In this paper, we review relevant works in
OMR, including fundamental methods and significant outcomes, and highlight
different stages of the OMR pipeline. These stages often lack standard input
and output representation and standardised evaluation. Therefore, comparing
different approaches and evaluating the impact of different processing methods
can become rather complex. This paper provides recommendations for future work,
addressing some of the highlighted issues and represents a position in
furthering this important field of research.
