Optical music recognition for scores written in white mensural notation
Journal on Image and Video Processing - Special issue on image and video processing for cultural heritage
Music score binarization based on domain knowledge
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
onNote: playing printed music scores as a musical instrument
Proceedings of the 24th annual ACM symposium on User interface software and technology
Note symbol recognition for music scores
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Writer identification in handwritten musical scores with bags of notes
Pattern Recognition
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
Automatic stave discovery for musical facsimiles
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization based approach. We define three different error metrics, compare the algorithms with respect to these metrics and measure their robustness with respect to certain image defects. Our test images are computer-generated scores on which we apply various image deformations typically found in real-world data. In addition to modern western music notation our test set also includes historic music notation such as mensural notation and lute tablature. Our general approach and evaluation methodology is not specific to staff removal, but applicable to other segmentation problems as well.