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Automatic Score Extraction with Optical Music Recognition (OMR)

  • Chapter
Springer Handbook of Systematic Musicology

Part of the book series: Springer Handbooks ((SHB))

Abstract

Optical music recognition (GlossaryTerm

OMR

) describes the process of automatically transcribing music notation from a digital image. Although similar to optical character recognition (GlossaryTerm

OCR

), the process and procedures of OMR diverge due to the fundamental differences between text and music notation, such as the two-dimensional nature of the notation system and the overlay of music symbols on top of staff lines. The OMR process can be described as a sequence of steps, with techniques adapted from disciplines including image processing, machine learning, grammars, and notation encoding. The sequence and specific techniques used can differ depending on the condition of the image, the type of notation, and the desired output.

Several commercial and open-source OMR software systems have been available since the mid-1990s. Most of them are designed to be used by individuals and recognize common (post-18th-century) Western music notation, though there have been some efforts to recognize other types of music notation such as for the lute and for earlier Western music.

Even though traditional applications of OMR have focused on small-scale recognition tasks, typically as an automated method of musical entry for score editing, new applications of large-scale OMR are under development, where automated recognition is the central technology for building full-music search systems, similar to the large-scale full-text recognition efforts.

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Abbreviations

k-NN:

k-nearest-neighbor

ASCII:

American standard code for information interchange

BD:

book-dependent

BI:

book-independent

CWMN:

common Western music notation

DARMS:

digital alternative representation of music scores

EsAC:

Essen associative code

GPL:

general public license

HMM:

hidden Markov model

HTML:

hyper-text markup language

MAP:

maximum a posteriori

MIDI:

musical instrument digital interface

NIFF:

notation interchange file format

NN:

neural network

OCR:

optical character recognition

ODD:

one document does it all

OMR:

optical music recognition

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Author information

Authors and Affiliations

  1. Schulich School of Music, McGill University, 555 Sherbrooke St. West, QC H3A1E3, Montreal, Canada

    Ichiro Fujinaga

  2. Bodleian Libraries, University of Oxford, Osney One Building, Osney Mead, OX2 0EW, Oxford, UK

    Andrew Hankinson

  3. Swiss RISM Office, Hallwylstr. 15, 3000, Bern, Switzerland

    Laurent Pugin

Authors
  1. Ichiro Fujinaga
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  2. Andrew Hankinson
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  3. Laurent Pugin
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Corresponding author

Correspondence to Ichiro Fujinaga .

Editor information

Editors and Affiliations

  1. Institute of Systematic Musicology, University of Hamburg, Neue Rabenstr. 13, 20354, Hamburg, Germany

    Rolf Bader

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Fujinaga, I., Hankinson, A., Pugin, L. (2018). Automatic Score Extraction with Optical Music Recognition (OMR). In: Bader, R. (eds) Springer Handbook of Systematic Musicology. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55004-5_16

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  • DOI: https://doi.org/10.1007/978-3-662-55004-5_16

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