Optical music recognition: State-of-the-art and open issues

• Autor(a) principal: Rebelo, Ana
• Data de Publicação: 2012
• Outros Autores: Fujinaga, Ichiro, Paszkiewicz, Filipe, Marcal, Andre R. S., Guedes, Carlos, Cardoso, Jaime S.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: http://hdl.handle.net/11328/2505; https://doi.org/10.1007/s13735-012-0004-6
• topic: Computer music; Image processing; Machine learning; Music performance

For centuries, music has been shared and remembered by two traditions: aural transmission and in the form of written documents normally called musical scores. Many of these scores exist in the form of unpublished manuscripts and hence they are in danger of being lost through the normal ravages of time. To preserve the music requires some form of typesetting or, ideally, a computer system that can automatically decode the symbolic images and create new scores. Programs analogous to optical character recognition systems called optical music recognition (OMR) systems have been under intensive development for many years. However, the results to date are far from ideal. Each of the proposed methods emphasizes different properties and therefore makes it difficult to effectively evaluate its competitive advantages. This article provides an overview of the literature concerning the automatic analysis of images of printed and handwritten musical scores. For self-containment and for the benefit of the reader, an introduction to OMR processing systems precedes the literature overview. The following study presents a reference scheme for any researcher wanting to compare new OMR algorithms against well-known ones.