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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/4ACGJTH
Repositorysid.inpe.br/sibgrapi/2023/12.13.12.16
Last Update2023:12.13.12.58.49 (UTC) vwnascimento@inf.ufpr.br
Metadata Repositorysid.inpe.br/sibgrapi/2023/12.13.12.16.03
Metadata Last Update2023:12.13.12.58.49 (UTC) vwnascimento@inf.ufpr.br
Citation KeyNascimentoLaroMeno:2023:SuToLi
TitleSuper-Resolution Towards License Plate Recognition
FormatOn-line
Year2023
Access Date2024, Apr. 29
Number of Files1
Size808 KiB
2. Context
Author1 Nascimento, Valfride
2 Laroca, Rayson
3 Menotti, David
Affiliation1 Universidade Federal do Paraná
2 Universidade Federal do Paraná
3 Universidade Federal do Paraná
EditorClua, Esteban Walter Gonzalez
Körting, Thales Sehn
Paulovich, Fernando Vieira
Feris, Rogerio
e-Mail Addressvwnascimento@inf.ufpr.br
Conference NameConference on Graphics, Patterns and Images, 36 (SIBGRAPI)
Conference LocationRio Grande, RS
DateNov. 06-09, 2023
Book TitleProceedings
Tertiary TypeMaster's or Doctoral Work
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsPixelShuffle
Reconstruction
Super-Resolution
AbstractRecent years have seen significant developments in license plate recognition through the integration of deep learning techniques and the increasing availability of training data. Nevertheless, reconstructing license plates from low-resolution surveillance footage remains challenging. To address this issue, we propose an attention-based super-resolution approach that incorporates sub-pixel convolution layers and an Optical Character Recognition (OCR)-based loss function. We trained the proposed architecture on synthetic images created by applying heavy Gaussian noise followed by bicubic downsampling to high-resolution license plate images. Our results show that the proposed approach for reconstructing these low-resolution images substantially outperforms existing methods in both quantitative and qualitative measures. Our source code is publicly available at https://github.com/valfride/lpr-rsr-ext/.
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/4ACGJTH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/4ACGJTH
Languageen
Target File2023_SIBGRAPI_WTD_Valfride.pdf
User Groupvwnascimento@inf.ufpr.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume
7. Description control
e-Mail (login)vwnascimento@inf.ufpr.br
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