期刊
出版社
IEEE
DOI: 10.1109/ICDAR.2017.88
关键词
-
资金
- Spanish project [TIN2014-52072-P]
- CERCA Programme/Generalitat de Catalunya
- NVIDIA Corporation
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.
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