PT Unknown AU Pau Riba Adria Molina Lluis Gomez Oriol Ramos Terrades Josep Llados TI Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting BT 16th International Conference on Document Analysis and Recognition PY 2021 BP 381–395 VL 12822 DI 10.1007/978-3-030-86331-9_25 AB 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. ER