Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting

Autor:	Riba, Pau, Molina, Adrià, Gomez, Lluis, Ramos-Terrades, Oriol, Lladós, Josep
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at ICDAR 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.05144 Zobrazit plný text záznamu View this record from Arxiv