An Effective Staff Detection and Removal Technique for Musical Documents

Musical staff line detection and removal techniques detect the staff positions in musical documents and segment musical score from musical documents by removing those staff lines. It is an important preprocessing step for ensuing the Optical Music Recognition tasks. This paper proposes an effective staff line detection and removal method that makes use of the global information of the musical document and models the staff line shape. It first estimates the staff height and space, and then models the shape of the staff line by examining the orientation of the staff pixels. At last the estimated model is used to find out the location of staff lines and hence to remove those detected staff lines. The proposed technique is simple, robust, and involves few parameters. It has been tested on the dataset of the recent staff removal competition held under the International Conference of Document Analysis and Recognition(ICDAR) 2011. Experimental results show the effectiveness and robustness of our proposed technique on musical documents with various types of deformations.

[1]  Sylvie Philipp-Foliguet,et al.  Printed music recognition , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[2]  Alicia Fornés,et al.  The ICDAR 2011 Music Scores Competition: Staff Removal and Writer Identification , 2011, 2011 International Conference on Document Analysis and Recognition.

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

[4]  Ichiro Fujinaga,et al.  Staff Detection and Removal , 2004 .

[5]  Hidetoshi Miyao,et al.  Stave Extraction for Printed Music Scores , 2002, IDEAL.

[6]  Umapada Pal,et al.  An Efficient Staff Removal Approach from Printed Musical Documents , 2010, 2010 20th International Conference on Pattern Recognition.

[7]  Ichiro Fujinaga,et al.  A Comparative Study of Staff Removal Algorithms , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  David Bainbridge,et al.  Dealing with superimposed objects in optical music recognition , 1997 .

[9]  Susan Ella George,et al.  Visual Perception of Music Notation: On-Line and Off-Line Recognition , 2004 .

[10]  Carlos Guedes,et al.  Staff Detection with Stable Paths , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.