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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3PFAFJL
Repositorysid.inpe.br/sibgrapi/2017/08.18.12.21
Last Update2017:08.18.12.21.50 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2017/08.18.12.21.50
Metadata Last Update2022:06.14.00.08.46 (UTC) administrator
DOI10.1109/SIBGRAPI.2017.14
Citation KeySilvaJung:2017:ReBrLi
TitleReal-Time Brazilian License Plate Detection and Recognition Using Deep Convolutional Neural Networks
FormatOn-line
Year2017
Access Date2024, Apr. 28
Number of Files1
Size2823 KiB
2. Context
Author1 Silva, Sergio Montazzolli
2 Jung, Claudio Rosito
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
e-Mail Addresssmsilva@inf.ufrgs.br
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ, Brazil
Date17-20 Oct. 2017
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2017-08-18 12:21:50 :: smsilva@inf.ufrgs.br -> administrator ::
2022-06-14 00:08:46 :: administrator -> :: 2017
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsLicense Plate
Convolutional Neural Networks
Deep Learning
AbstractAutomatic License Plate Recognition (ALPR) is an important task with many applications in Intelligent Transportation and Surveillance systems. As in other computer vision tasks, Deep Learning (DL) methods have been recently applied in the context of ALPR, focusing on country-specific plates, such as American or European, Chinese, Indian and Korean. However, either they are not a complete DL-ALPR pipeline, or they are commercial and utilize private datasets and lack detailed information. In this work, we proposed an end-to-end DL-ALPR system for Brazilian license plates based on state-of-the-art Convolutional Neural Network architectures. Using a publicly available dataset with Brazilian plates, the system was able to correctly detect and recognize all seven characters of a license plate in 63.18% of the test set, and 97.39% when considering at least five correct characters (partial match). Considering the segmentation and recognition of each character individually, we are able to segment 99% of the characters, and correctly recognize 93% of them.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Real-Time Brazilian License...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Real-Time Brazilian License...
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source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3PFAFJL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3PFAFJL
Languageen
Target Filereal-time-brazilian (3).pdf
User Groupsmsilva@inf.ufrgs.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3PKCC58
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2017/09.12.13.04 5
sid.inpe.br/sibgrapi/2022/06.10.21.49 2
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsaffiliation archivingpolicy 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


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