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dc.contributor.authorSabir, Ahmed
dc.contributor.authorMoreno-Noguer, Francesc
dc.contributor.authorPadró, Lluís
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Intel·ligència Artificial
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2020-10-01T07:36:01Z
dc.date.available2020-10-01T07:36:01Z
dc.date.issued2020
dc.identifier.citationSabir, A.; Moreno-Noguer, F.; Padró, L. Textual visual semantic dataset for text spotting. A: IEEE Conference on Computer Vision and Pattern Recognition. "2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops: Virtual, 14-19 June 2020: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 2306-2315. ISBN 978-1-7281-9360-1. DOI 10.1109/CVPRW50498.2020.00279.
dc.identifier.isbn978-1-7281-9360-1
dc.identifier.otherhttps://arxiv.org/pdf/2004.10349.pdf
dc.identifier.urihttp://hdl.handle.net/2117/329587
dc.description.abstractText Spotting in the wild consists of detecting and recognizing text appearing in images (e.g. signboards, traffic signals or brands in clothing or objects). This is a challenging problem due to the complexity of the context where texts appear (uneven backgrounds, shading, occlusions, perspective distortions, etc.). Only a few approaches try to exploit the relation between text and its surrounding environment to better recognize text in the scene. In this paper, we propose a visual context dataset1 for Text Spotting in the wild, where the publicly available dataset COCO-text [40] has been extended with information about the scene (such as objects and places appearing in the image) to enable researchers to include semantic relations between texts and scene in their Text Spotting systems, and to offer a common framework for such approaches. For each text in an image, we extract three kinds of context information: objects in the scene, image location label and a textual image description (caption). We use state-of-the-art out-of-the-box available tools to extract this additional information. Since this information has textual form, it can be used to leverage text similarity or semantic relation methods into Text Spotting systems, either as a post-processing or in an end-to-end training strategy.
dc.description.sponsorshipThis work is supported by the KASP Scholarship Program and by the Spanish government under projects HuMoUR TIN2017-90086-R and María de Maeztu Seal of Excellence MDM-2016-0656.
dc.format.extent10 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.lcshData mining
dc.subject.lcshImage analysis
dc.subject.otherText spotting
dc.subject.otherDeep learning
dc.subject.otherDataset
dc.titleTextual visual semantic dataset for text spotting
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacMineria de dades
dc.subject.lemacImatges -- Anàlisi
dc.contributor.groupUniversitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
dc.contributor.groupUniversitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
dc.identifier.doi10.1109/CVPRW50498.2020.00279
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/abstract/document/9150617
dc.rights.accessOpen Access
local.identifier.drac28881915
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/MDM-2016-0656
local.citation.authorSabir, A.; Moreno-Noguer, F.; Padró, L.
local.citation.contributorIEEE Conference on Computer Vision and Pattern Recognition
local.citation.publicationName2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops: Virtual, 14-19 June 2020: proceedings
local.citation.startingPage2306
local.citation.endingPage2315


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