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Author (up) Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas edit   pdf
doi  openurl
  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  
  Volume Issue Pages  
  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  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ GRK2017 Serial 2999  
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