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Author Pau Riba; Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados edit   pdf
doi  openurl
  Title Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting Type Conference Article
  Year 2021 Publication (up) 16th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume 12822 Issue 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.  
  Address Lausanne; Suissa; September 2021  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.121; 600.140; 110.312 Approved no  
  Call Number Admin @ si @ RMG2021 Serial 3572  
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