Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting

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

*Corresponding author for this work

Research output: Book/ReportProceedingResearchpeer-review


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.

Original languageEnglish
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
Publication statusPublished - 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12822 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Ranking loss
  • Smooth-AP
  • Smooth-nDCG
  • Word spotting


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