An online handwritten music symbol recognition system

Authors:
Hidetoshi Miyao;Minoru Maruyama
Affiliations:
Shinshu University, Department of Information Engineering, Faculty of Engineering, 4–17–1 Wakasato, 380–8553, Nagano, Japan;Shinshu University, Department of Information Engineering, Faculty of Engineering, 4–17–1 Wakasato, 380–8553, Nagano, Japan
Venue:
International Journal on Document Analysis and Recognition
Year:
2007

Citing 0
Cited 5

Estimating HMM Parameters Using Particle Swarm Optimisation

EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Eager interpretation of on-line hand-drawn structured documents: The DALI methodology

Pattern Recognition
Music alphabet for low-resolution touch displays

Proceedings of the International Conference on Advances in Computer Enterntainment Technology
onNote: playing printed music scores as a musical instrument

Proceedings of the 24th annual ACM symposium on User interface software and technology
The 2012 music scores competitions: staff removal and writer identification

GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges

Quantified Score

Hi-index	0.01

Visualization

Abstract

The objective of this study is to produce a system that would allow music symbols to be written by hand using a pen-based computer that would simulate the feeling of writing on sheets of paper and that would also accurately recognize the music symbols. To accomplish these objectives, the following methods are proposed: (1) Two features, time-series data and an image of a handwritten stroke, are used to recognize strokes; and (2) The strokes are combined, as efficiently as possible, and outputted automatically as a music symbol. As a result, recognition rates of 97.60 and 98.80% were obtained in tests with strokes and music symbols, respectively.