@Article{AliciaFornes2012,
author="Alicia Fornes
and Anjan Dutta
and Albert Gordo
and Josep Llados",
title="CVC-MUSCIMA: A Ground-Truth of Handwritten Music Score Images for Writer Identification and Staff Removal",
journal="International Journal on Document Analysis and Recognition",
year="2012",
volume="15",
number="3",
pages="243--251",
optkeywords="Music scores",
optkeywords="Handwritten documents",
optkeywords="Writer identification",
optkeywords="Staff removal",
optkeywords="Performance evaluation",
optkeywords="Graphics recognition",
optkeywords="Ground truths",
abstract="0,405JCRThe analysis of music scores has been an active research field in the last decades. However, there are no publicly available databases of handwritten music scores for the research community. In this paper we present the CVC-MUSCIMA database and ground-truth of handwritten music score images. The dataset consists of 1,000 music sheets written by 50 different musicians. It has been especially designed for writer identification and staff removal tasks. In addition to the description of the dataset, ground-truth, partitioning and evaluation metrics, we also provide some base-line results for easing the comparison between different approaches.",
optnote="DAG",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2129), last updated on Wed, 23 Jul 2014 10:23:43 +0200",
issn="1433-2833",
doi="10.1007/s10032-011-0168-2",
file=":http://refbase.cvc.uab.es/files/FDG2012.pdf:PDF"
}