%0 Journal Article
%T On the Influence of Word Representations for Handwritten Word Spotting in Historical  Documents
%A Josep Llados
%A Marçal Rusiñol
%A Alicia Fornes
%A David Fernandez
%A Anjan Dutta
%J International Journal of Pattern Recognition and Artificial Intelligence
%D 2012
%V 26
%N 5
%F Josep Llados2012
%O DAG
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2128), last updated on Thu, 13 Mar 2014 13:18:56 +0100
%X 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advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents.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.
%K Handwriting recognition
%K word spotting
%K historical documents
%K feature representation
%K shape descriptors   Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001412630025
%U http://refbase.cvc.uab.es/files/LRF2012.pdf
%U http://dx.doi.org/10.1142/S0218001412630025
%P 1263002-126027