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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.
Segmentation, Pixel classification, Music (Optical Recognition), Performance evaluation

I. Fujinaga, M. Droettboom, C. Dalitz and B. Pranzas, "A Comparative Study of Staff Removal Algorithms," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 753-766, 2007.
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