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Author (up) Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas
Title LSDE: Levenshtein Space Deep Embedding for Query-by-string Word Spotting Type Conference Article
Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal
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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’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.
Address Kyoto; Japan; November 2017
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Notes DAG; 600.084; 600.121 Approved no
Call Number Admin @ si @ GRK2017 Serial 2999
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