"A Machine Learning Based Method for Staff Removal"
Igor S. Montagner, Nina S. T. Hirata, Roberto Hirata Jr.
Abstract: Staff line removal is an important pre-processing step to convert content of
music score images to machine readable formats. Many heuristic algorithms have
been proposed for staff removal and recently a competition was organized in the
2013 ICDAR/GREC conference. Music score images are often subject to different
deformations and variations, and existing algorithms do not work well for all
cases. We investigate the application of a machine learning based method for the
staff removal problem. The method consists in learning multiple image operators
from training input-output pairs of images and then combining the results of
these operators. Each operator is based on local information provided by a
neighborhood window, which is usually manually chosen based on the content
of the images.
We propose a feature selection based approach for
automatically defining the windows and also for combining the operators. The
performance of the proposed method is superior to several existing methods and
is comparable to the best method in the competition.
All the operators executed in our paper were trained with TRIOSlib.
See our guide to learn how to use the
operators in this page.
The 12 windows below were determined automatically from a set of 70 images from the Training Subset 3.
The following windows were carefuly selected manually for the staff removal task. They can be used as baseline
performance for our window selection method.