@inbook{3ff772abfefb4f1b8e6814029a029995,
title = "LSDE: Levenshtein Space Deep Embedding for Query-by-String Word Spotting",
abstract = "In 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'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.",
keywords = "CNNs, Deep embeddings, Handwritten Keyword Spotting, Query by string",
author = "Lluis Gomez and Marcal Rusinol and Dimosthenis Karatzas",
note = "Funding Information: This work was supported by the Spanish project TIN2014-52072-P and by the CERCA Programme / Generalitat de Catalunya. We gratefully acknowledgethe support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research. Publisher Copyright: {\textcopyright} 2017 IEEE.",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICDAR.2017.88",
language = "English",
series = "Proceedings of the International Conference on Document Analysis and Recognition, ICDAR",
pages = "499--504",
booktitle = "Proceedings - 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017",
}