TY  - CONF
AU  - Lluis Gomez
AU  - Marçal Rusiñol
AU  - Dimosthenis Karatzas
A2  - ICDAR
PY  - 2017//
TI  - LSDE: Levenshtein Space Deep Embedding for Query-by-string Word Spotting
BT  - 14th International Conference on Document Analysis and Recognition
N2  - 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.
L1  - http://refbase.cvc.uab.es/files/GRK2017.pdf
UR  - http://dx.doi.org/10.1109/ICDAR.2017.88
N1  - DAG; 600.084; 600.121
ID  - Lluis Gomez2017
ER  -