@InProceedings{PauRiba2021,
author="Pau Riba
and Adria Molina
and Lluis Gomez
and Oriol Ramos Terrades
and Josep Llados",
title="Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting",
booktitle="16th International Conference on Document Analysis and Recognition",
year="2021",
volume="12822",
pages="381--395",
abstract="In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work.",
optnote="DAG; 600.121; 600.140; 110.312",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3572), last updated on Mon, 24 Oct 2022 15:15:24 +0200",
doi="10.1007/978-3-030-86331-9_25",
file=":http://refbase.cvc.uab.es/files/RMG2021.pdf:PDF"
}