Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2015: (UTC) administrator
Metadata Last Update2016: (UTC) administrator
Citation KeyGonçalvesGomeSchw:2015:LiPlCh
TitleLicense plate character segmentation using Partial Least Squares
Access Date2021, Dec. 07
Number of Files1
Size1134 KiB
Context area
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
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 :: -> administrator ::
2016-06-03 21:18:36 :: administrator -> :: 2015
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
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. > SDLA > SIBGRAPI 2015 > License plate character...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 13/07/2015 11:40 1.1 KiB 
Conditions of access and use area
data URL
zipped data URL
Target File2015-Sibgrapi-SegPlate.pdf
User Groupadministrator
Update Permissionnot transferred
Allied materials area
Next Higher Units8JMKD3MGPBW34M/3K24PF8
Notes area
Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi edition electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume