Optical Music Recognition: State of the Art and Major Challenges
It addresses the problem of transcribing sheet music into machine-readable formats for musicians, archivists, and digital musicologists, but is incremental as it reviews existing work and provides recommendations.
The paper reviews the state of the art in Optical Music Recognition (OMR), highlighting a shift from conventional computer vision to deep learning approaches, and identifies challenges such as lack of standardized evaluation and input/output representations.
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.