An adaptive staff line removal in music score images

This paper proposes an adaptive staff line removal method for music score images. The staff line removal is a key procedure in Optical Music Recognition (OMR) before musical symbol segmentation and classification. The proposed method consists of four main procedures, i.e., ROI(Region of Interest) selection, candidate point extraction, rotation correction and staff line removal. It selects a ROI, extracts candidate points in the selected ROI and estimates rotation angle with these candidate points. After rotation correction, the staff line width is estimated using horizontal projection and the staff lines are removed correspondingly. Extensive experiments show that the proposed method is robust for both printed and scanned music score images.

[1]  Amnon Wolman,et al.  Optical Music Recognition - progress report , 1994, ICMC.

[2]  Bruce W. Pennycook,et al.  Adaptive optical music recognition , 1997 .

[3]  Paolo Nesi,et al.  Projection based segmentation of musical sheets , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[4]  Laurent Pugin,et al.  Optical Music Recognitoin of Early Typographic Prints using Hidden Markov Models , 2006, ISMIR.

[5]  Henry S. Baird,et al.  A Critical Survey of Music Image Analysis , 1992 .

[6]  Isabel Barbancho,et al.  Optical Music Recognition for Scores Written in White Mensural Notation , 2009, EURASIP J. Image Video Process..

[7]  Laurent Pugin,et al.  Optical Music Recognition of Early Typographic Prints using Hidden Markov Models , 2006 .

[8]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[9]  Etienne Sicard.  An efficient method for the recognition of printed music , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[10]  Florence Rossant,et al.  A global method for music symbol recognition in typeset music sheets , 2002, Pattern Recognit. Lett..

[11]  Timothy C. Bell,et al.  The Challenge of Optical Music Recognition , 2001, Comput. Humanit..

[12]  Robert M. Haralick,et al.  MUSER: A prototype musical score recognition system using mathematical morphology , 1993, Machine Vision and Applications.