Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting

Pau Riba*, Adrià Molina, Lluis Gomez, Oriol Ramos-Terrades, Josep Lladós

*Autor corresponent d’aquest treball

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Resum

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.

Idioma originalAnglès
EditorSpringer Science and Business Media Deutschland GmbH
Nombre de pàgines15
DOIs
Estat de la publicacióPublicada - 2021

Sèrie de publicacions

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volum12822 LNCS
ISSN (imprès)0302-9743
ISSN (electrònic)1611-3349

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