@Inbook{AliciaFornes2014,
author="Alicia Fornes
and V.C.Kieu
and M. Visani
and N.Journet
and Anjan Dutta",
editor="B.Lamiroy
and J.-M. Ogier",
chapter="The ICDAR/GREC 2013 Music Scores Competition: Staff Removal",
title="Graphics Recognition. Current Trends and Challenges",
year="2014",
publisher="Springer Berlin Heidelberg",
volume="8746",
pages="207--220",
optkeywords="Competition",
optkeywords="Graphics recognition",
optkeywords="Music scores",
optkeywords="Writer identification",
optkeywords="Staff removal",
abstract="The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated in both staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario concerning old and degraded music scores. For this purpose, we have generated a new set of semi-synthetic images using two degradation models that we previously introduced: local noise and 3D distortions. In this extended paper we provide an extended description of the dataset, degradation models, evaluation metrics, the participant{\textquoteright}s methods and the obtained results that could not be presented at ICDAR and GREC proceedings due to page limitations.",
optnote="DAG; 600.077; 600.061",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2581), last updated on Wed, 16 Dec 2015 10:49:36 +0100",
isbn="978-3-662-44853-3",
issn="0302-9743",
doi="10.1007/978-3-662-44854-0_16"
}