Adaptive line fitting for staff detection in handwritten music score images

Abstract

The target of staff line detection is to extract staff lines accurately in order to remove them while preserves the shape of musical symbols. There are several researches in staff line detection and removal which provide good results with printed scores. However, in case of handwritten music scores, detecting staff lines still has problems due to the diversity of musical symbol shape, line curvature and disconnection. In this paper, we present a novel line fitting method for detecting the staff line in handwritten music score images. Our method first starts with the estimation of staff line height and staff space height. Then the staff segments are selected. Based on these staff candidates, we construct a line with the orientation of the staff segment and gradually fit it to the real lines. The staff line is then removed and the process is continuing until no line is detected. To show the effectiveness of our proposed method with different types of handwritten music score, images from the ICDAR/GREC 2013 dataset are tested. The experiment results show the advantages of our algorithm comparing with the previous approaches.