Music Score Binarization Based on Domain Knowledge

Image binarization is a common operation in the preprocessing stage in most Optical Music Recognition (OMR) systems. The choice of an appropriate binarization method for handwritten music scores is a difficult problem. Several works have already evaluated the performance of existing binarization processes in diverse applications. However, no goal-directed studies for music sheets documents were carried out. This paper presents a novel binarization method based in the content knowledge of the image. The method only needs the estimation of the staffline thickness and the vertical distance between two stafflines. This information is extracted directly from the gray level music score. The proposed binarization procedure is experimentally compared with several state of the art methods.

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

[2]  Pheng-Ann Heng,et al.  A double-threshold image binarization method based on edge detector , 2008, Pattern Recognit..

[3]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[4]  Alicia Fornés,et al.  On the Use of Textural Features for Writer Identification in Old Handwritten Music Scores , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[5]  W. Guitang,et al.  A new method for image segmentation , 2009, 2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA).

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

[7]  Adnan Khashman,et al.  A Novel Thresholding Method for Text Separation and Document Enhancement , 2007 .

[8]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[9]  Mao-Jiun J. Wang,et al.  Image thresholding by minimizing the measures of fuzzines , 1995, Pattern Recognit..

[10]  Márcio Portes de Albuquerque,et al.  Image thresholding using Tsallis entropy , 2004, Pattern Recognit. Lett..

[11]  Øivind Due Trier,et al.  Evaluation of Binarization Methods for Document Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Wayne Niblack,et al.  An introduction to digital image processing , 1986 .

[13]  Yan Chen,et al.  Comparison of some thresholding algorithms for text/background segmentation in difficult document images , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[14]  Alicia Fornés,et al.  Writer Identification in Old Handwritten Music Scores , 2008, 2008 The Eighth IAPR International Workshop on Document Analysis Systems.

[15]  Du-Ming Tsai,et al.  A fast thresholding selection procedure for multimodal and unimodal histograms , 1995, Pattern Recognit. Lett..

[16]  Jaime S. Cardoso,et al.  Robust Staffline Thickness and Distance Estimation in Binary and Gray-Level Music Scores , 2010, 2010 20th International Conference on Pattern Recognition.

[17]  Prasanna K. Sahoo,et al.  Threshold selection using Renyi's entropy , 1997, Pattern Recognit..

[18]  A. Khashman,et al.  Novel Thresholding Method for Document Analysis , 2006, 2006 IEEE International Conference on Industrial Technology.

[19]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.