TY  - CONF
AU  - Pau Riba
AU  - Adria Molina
AU  - Lluis Gomez
AU  - Oriol Ramos Terrades
AU  - Josep Llados
A2  - ICDAR
PY  - 2021//
TI  - Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting
BT  - 16th International Conference on Document Analysis and Recognition
SP  - 381–395
VL  - 12822
N2  - 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.
L1  - http://refbase.cvc.uab.es/files/RMG2021.pdf
UR  - http://dx.doi.org/10.1007/978-3-030-86331-9_25
N1  - DAG; 600.121; 600.140; 110.312
ID  - Pau Riba2021
ER  -