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dc.contributor.authorDecker, Luis
dc.contributor.authorPinto, Allan
dc.contributor.authorCampana, Jose
dc.contributor.authorNeira, Manuel
dc.contributor.authorSantos, Andreza
dc.contributor.authorConceição, Jhonatas
dc.contributor.authorAngeloni, Marcus
dc.contributor.authorLi, Lin
dc.contributor.authorTorres, Ricardo Da Silva
dc.date.accessioned2021-03-12T08:03:33Z
dc.date.available2021-03-12T08:03:33Z
dc.date.created2020-04-26T10:47:50Z
dc.date.issued2020
dc.identifier.citationVISIGRAPP. 2020, 5 343-350.en_US
dc.identifier.issn2184-5921
dc.identifier.urihttps://hdl.handle.net/11250/2733029
dc.description.abstractAbstract: Multiple research initiatives have been reported to yield highly effective results for the text detection problem. However, most of those solutions are very costly, which hamper their use in several applications that rely on the use of devices with restrictive processing power, like smartwatches and mobile phones. In this paper, we address this issue by investigating the use of efficient object detection networks for this problem. We propose the combination of two light architectures, MobileNetV2 and Single Shot Detector (SSD), for the text detection problem. Experimental results in the ICDAR’11 and ICDAR’13 datasets demonstrate that our solution yields the best trade-off between effectiveness and efficiency and also achieved the state-of-the-art results in the ICDAR’11 dataset with an f-measure of 96.09%.en_US
dc.language.isoengen_US
dc.publisherSciTePressen_US
dc.titleMobText: A Compact Method for Scene Text Localizationen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber343-350en_US
dc.source.volume5en_US
dc.source.journalVISIGRAPPen_US
dc.identifier.doi10.5220/0008954103430350
dc.identifier.cristin1808066
dc.description.localcodeThis article will not be available due to copyright restrictions (c) 2020 by SciTePressen_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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