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A multi-layer image operator learning based on sample structure for staff lines removal
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DOI：10.1007/s10489-022-03929-y.png)
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The removal of staff lines is the most significant step to separate notes from the score images in optical music recognition (OMR). However, musical images are often affected by different deformations, and it is difficult to delete the staff lines completely without affecting the integrity of the notes. A novel multi-layer image operator learning algorithm based on sample structure is proposed in this paper to solve the problem of staff lines deletion. Our algorithm is dedicated to obtain the structural characteristics of staff lines via image operator learning. Firstly, an iterative strategy is proposed to update the distribution of the samples for learning multiple image operators with different sample structure features. Further, based on the learned image operators, a multi-layer image operators network is designed to obtain the optimal combination of multiple operators. Finally, we have verified the feasibility of our algorithm on the data set 2013 ICDAR/GREC staff lines removal competition. The experiment shows that the proposed algorithm is robust against many kinds of deformation. Moreover, our algorithm is more competitive by comparing with state-of-the-art algorithms.
Keyword:
Staff lines removal
Optical music recognition
Image operators learning
Document image processing
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