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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPBW34M/3JRK5BH
Repositorysid.inpe.br/sibgrapi/2015/07.13.14.40
Last Update2015:07.13.14.40.06 (UTC) administrator
Metadatasid.inpe.br/sibgrapi/2015/07.13.14.40.06
Metadata Last Update2016:06.03.21.18.36 (UTC) administrator
Citation KeyGonçalvesGomeSchw:2015:LiPlCh
TitleLicense plate character segmentation using Partial Least Squares
FormatOn-line
Year2015
Access Date2021, Dec. 07
Number of Files1
Size1134 KiB
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Author1 Gonçalves, Gabriel Resende
2 Gomes, David Menotti
3 Schwartz, William Robson
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Ouro Preto
3 Universidade Federal de Minas Gerais
EditorRios, Ricardo Araujo
Paiva, Afonso
e-Mail Addressgabrielrg@dcc.ufmg.br
Conference NameConference on Graphics, Patterns and Images, 28 (SIBGRAPI)
Conference LocationSalvador
DateAug. 26-29, 2015
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleProceedings
Tertiary TypeWork in Progress
History (UTC)2015-07-13 14:40:06 :: gabrielrg@dcc.ufmg.br -> administrator ::
2016-06-03 21:18:36 :: administrator -> :: 2015
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Is the master or a copy?is the master
Content Stagecompleted
Transferable1
KeywordsAutomatic license plate recognition
character segmentation
partial least squares
AbstractA very important research topic nowadays is the Automatic License Plate Recognition (ALPC). This task consists in locating and identifying an on-track vehicle automatically. This task can be divided into the following subtasks: vehicle detection, license plate detection, characters segmentation and character recognition. This work proposes a new technique to perform character segmentation, which is considered solved in the literature, but in practice is a bottleneck for achieving a robust ALPC system. Our approach is a learning-based technique that uses a regression method known as Partial Least Squares to find the best points where the segmentation should be done between the characters. We perform experiments using a dataset composed of 2,000 license plates and three baselines to compare them with the results obtained by the proposed approach. In addition, we evaluate the usage of the PLS with five feature descriptors and our results show that our method is able to achieve a result up to 46.5% of accuracy, evaluated by the Jaccard measure.
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data URLhttp://urlib.net/ibi/8JMKD3MGPBW34M/3JRK5BH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW34M/3JRK5BH
Languageen
Target File2015-Sibgrapi-SegPlate.pdf
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gabrielrg@dcc.ufmg.br
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Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPBW34M/3K24PF8
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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