@Article{JosepLlados2012,
author="Josep Llados
and Mar{\c{c}}al Rusi{\~n}ol
and Alicia Fornes
and David Fernandez
and Anjan Dutta",
title="On the Influence of Word Representations for Handwritten Word Spotting in Historical  Documents",
journal="International Journal of Pattern Recognition and Artificial Intelligence",
year="2012",
volume="26",
number="5",
pages="1263002-126027",
optkeywords="Handwriting recognition",
optkeywords="word spotting",
optkeywords="historical documents",
optkeywords="feature representation",
optkeywords="shape descriptors   Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001412630025",
abstract="0,624 JCRWord spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images.",
optnote="DAG",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2128), last updated on Thu, 13 Mar 2014 13:18:56 +0100",
doi="10.1142/S0218001412630025",
file=":http://refbase.cvc.uab.es/files/LRF2012.pdf:PDF"
}