Learning to remove staff lines from music score images

The methods for removal of staff lines rely on characteristics specific to musical documents and they are usually not robust to some types of imperfections in the images. To overcome this limitation, we propose the use of binary morphological operator learning, a technique that estimates a local operator from a set of example images. Experimental results in both synthetic and real images show that our approach can adapt to different types of deformations and achieves similar or better performance than existing methods in most of the test scenarios.