@InProceedings{LluisGomez2017,
author="Lluis Gomez
and Mar{\c{c}}al Rusi{\~n}ol
and Dimosthenis Karatzas",
title="LSDE: Levenshtein Space Deep Embedding for Query-by-string Word Spotting",
booktitle="14th International Conference on Document Analysis and Recognition",
year="2017",
abstract="n this paper we present the LSDE string representation and its application to handwritten word spotting. LSDE is a novel embedding approach for representing strings that learns a space in which distances between projected points are correlated with the Levenshtein edit distance between the original strings.We show how such a representation produces a more semantically interpretable retrieval from the user{\textquoteright}s perspective than other state of the art ones such as PHOC and DCToW. We also conduct a preliminary handwritten word spotting experiment on the George Washington dataset.",
optnote="DAG; 600.084; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2999), last updated on Mon, 07 Dec 2020 14:28:43 +0100",
doi="10.1109/ICDAR.2017.88",
file=":http://refbase.cvc.uab.es/files/GRK2017.pdf:PDF"
}