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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/43AL935
Repositorysid.inpe.br/sibgrapi/2020/09.25.12.14
Last Update2020:09.25.12.14.30 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2020/09.25.12.14.30
Metadata Last Update2022:06.14.00.00.06 (UTC) administrator
DOI10.1109/SIBGRAPI51738.2020.00037
Citation KeyZeniJung:2020:WeSuCh
TitleWeakly Supervised Character Detection for License Plate Recognition
FormatOn-line
Year2020
Access Date2024, Apr. 29
Number of Files1
Size885 KiB
2. Context
Author1 Zeni, Luis Felipe
2 Jung, Claudio
Affiliation1 Universidade Federal do Rio Grande do Sul
2 Universidade Federal do Rio Grande do Sul
EditorMusse, Soraia Raupp
Cesar Junior, Roberto Marcondes
Pelechano, Nuria
Wang, Zhangyang (Atlas)
e-Mail Addressluis.zeni@inf.ufrgs.br
Conference NameConference on Graphics, Patterns and Images, 33 (SIBGRAPI)
Conference LocationPorto de Galinhas (virtual)
Date7-10 Nov. 2020
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2020-09-25 12:14:30 :: luis.zeni@inf.ufrgs.br -> administrator ::
2022-06-14 00:00:06 :: administrator -> luis.zeni@inf.ufrgs.br :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsweakly supervised character detection
licence plate recognition
character detection
neural networks
AbstractAutomatic Licence Plate Recognition (ALPR) is an essential task in the context of intelligent transportation systems. In a typical ALPR pipeline, the last stage receives as input a cropped license plate region and outputs the string with the plate characters. This paper presents a Weakly Supervised Character Detection (WSCD) approach that requires only string-level annotations (as in generic text recognition methods) but is able to detect characters individually (as in detection-based methods, which require character-level annotations). The proposed method is evaluated in five distinct datasets and present very competitive results against other state-of-the-art methods.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2020 > Weakly Supervised Character...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Weakly Supervised Character...
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/43AL935
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/43AL935
Languageen
Target File21.pdf
User Groupluis.zeni@inf.ufrgs.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/43G4L9S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2020/10.28.20.46 4
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume
7. Description control
e-Mail (login)luis.zeni@inf.ufrgs.br
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