Learning global and local features for license plate detection

Sheng Wang, Wenjing Jia, Qiang Wu, Xiangjian He, Jie Yang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


This paper proposes an intelligent system that is capable of automatically detecting license plates from static images captured by a digital still camera. A supervised learning approach is used to extract features from license plates, and both global feature and local feature are organized into a cascaded structure. In general, our framework can be divided into two stages. The first stage is constructed by extracting global correlation features and a posterior probability can be estimated to quickly determine the degree of resemblance between the evaluated image region and a license plate. The second stage is constructed by further extracting local dense-SIFT (dSIFT) features for AdaBoost supervised learning approach, and the selected dSIFT features will be used to construct a strong classifier. Using dSIFT as a type of highly distinctive local feature, our algorithm gives high detection rate under various complex conditions. The proposed framework is compared with existing works and promising results are obtained.

Original languageEnglish
Title of host publicationNeural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
Number of pages10
EditionPART 3
Publication statusPublished - 2011
Externally publishedYes
Event18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai, China
Duration: 13 Nov 201117 Nov 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7064 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th International Conference on Neural Information Processing, ICONIP 2011


  • AdaBoost
  • Intelligent system
  • License plate

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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