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Author | 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. |
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Address | Kyoto; Japan; November 2017 | ||||
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Language | Summary Language | Original Title | |||
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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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