@Article{Mar{\c c}alRusi{\~n}ol2010,
author="Mar{\c{c}}al Rusi{\~n}ol
and Agnes Borras
and Josep Llados",
title="Relational Indexing of Vectorial Primitives for Symbol Spotting in Line-Drawing Images",
journal="Pattern Recognition Letters",
year="2010",
publisher="Elsevier",
volume="31",
number="3",
pages="188--201",
optkeywords="Document image analysis and recognition",
optkeywords="Graphics recognition",
optkeywords="Symbol spotting",
optkeywords="Vectorial representations",
optkeywords="Line-drawings",
abstract="This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an off-the-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results.",
optnote="DAG",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1177), last updated on Thu, 21 Jun 2012 12:32:05 +0200",
doi="10.1016/j.patrec.2009.10.002"
}