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dc.contributor.authorCampana, Jose
dc.contributor.authorPinto, Allan
dc.contributor.authorNeira, Manuel
dc.contributor.authorDecker, Luis
dc.contributor.authorSantos, Andreza
dc.contributor.authorConceição, Jhonatas
dc.contributor.authorTorres, Ricardo Da Silva
dc.identifier.citationIEEE Access. 2020, 8 81257-81270.en_US
dc.description.abstractHundreds of text detection methods have been proposed, motivated by their widespread use in several applications. Despite the huge progress in the area, which includes even the use of sophisticated learning schemes, ad-hoc post-processing procedures are often employed to improve the text detection rate, by removing both false positives and negatives. Another issue refers to the lack of the use of the complementary views provided by different text detection methods. This paper aims to fill these gaps. We propose the use of a soft computing framework, based on genetic programming (GP), to guide the definition of suitable post-processing procedures through the combination of basic operators, which may be applied to improve detection results provided by multiple methods at the same time. Performed experiments in the widely used ICDAR 2011, ICDAR 2013, and ICDAR 2015 datasets demonstrate that our GP-based approach leads to F1 effectiveness gains up to 5.1 percentage points, when compared to several baselines.en_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleOn the Fusion of Text Detection Results: A Genetic Programming Approachen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.subject.nsiVDP::Informasjons- og kommunikasjonsteknologi: 550en_US
dc.subject.nsiVDP::Information and communication technology: 550en_US
dc.source.journalIEEE Accessen_US
dc.description.localcodeThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal