1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPAW/3PFAFJL |
Repository | sid.inpe.br/sibgrapi/2017/08.18.12.21 |
Last Update | 2017:08.18.12.21.50 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2017/08.18.12.21.50 |
Metadata Last Update | 2022:06.14.00.08.46 (UTC) administrator |
DOI | 10.1109/SIBGRAPI.2017.14 |
Citation Key | SilvaJung:2017:ReBrLi |
Title | Real-Time Brazilian License Plate Detection and Recognition Using Deep Convolutional Neural Networks |
Format | On-line |
Year | 2017 |
Access Date | 2024, Oct. 15 |
Number of Files | 1 |
Size | 2823 KiB |
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2. Context | |
Author | 1 Silva, Sergio Montazzolli 2 Jung, Claudio Rosito |
Editor | Torchelsen, 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 Address | smsilva@inf.ufrgs.br |
Conference Name | Conference on Graphics, Patterns and Images, 30 (SIBGRAPI) |
Conference Location | Niterói, RJ, Brazil |
Date | 17-20 Oct. 2017 |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Book Title | Proceedings |
Tertiary Type | Full Paper |
History (UTC) | 2017-08-18 12:21:50 :: smsilva@inf.ufrgs.br -> administrator :: 2022-06-14 00:08:46 :: administrator -> :: 2017 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Version Type | finaldraft |
Keywords | License Plate Convolutional Neural Networks Deep Learning |
Abstract | Automatic 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 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2017 > Real-Time Brazilian License... |
Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > Real-Time Brazilian License... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPAW/3PFAFJL |
zipped data URL | http://urlib.net/zip/8JMKD3MGPAW/3PFAFJL |
Language | en |
Target File | real-time-brazilian (3).pdf |
User Group | smsilva@inf.ufrgs.br |
Visibility | shown |
Update Permission | not transferred |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPAW/3PKCC58 8JMKD3MGPEW34M/4742MCS |
Citing Item List | sid.inpe.br/sibgrapi/2017/09.12.13.04 32 sid.inpe.br/sibgrapi/2022/06.10.21.49 4 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | affiliation 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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