Staff-line detection on grayscale images with pixel classification

Abstract

Staff-line detection and removal are important processing steps in most Optical Music Recognition systems. Traditional methods make use of heuristic strategies based on image processing techniques with binary images. However, binarization is a complex process for which it is difficult to achieve perfect results. In this paper we describe a novel staff-line detection and removal method that deals with grayscale images directly. Our approach uses supervised learning to classify each pixel of the image as symbol, staff, or background. This classification is achieved by means of Convolutional Neural Networks. The features of each pixel consist of a square window from the input image centered at the pixel to be classified. As a case of study, we performed experiments with the CVC-Muscima dataset. Our approach showed promising performance, outperforming state-of-the-art algorithms for staff-line removal.