Number plate recognition based on support vector machines

Zheng Lihong, He Xiangjian

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

29 Citations (Scopus)

Abstract

Automatic number plate recognition method is required due to increasing traffic management. In this paper, we first briefly review some knowledge of Support Vector Machines (SVMs). Then a number plate recognition algorithm is proposed. This algorithm employs an SVM to recognize numbers. The algorithm starts from a collection of samples of numbers from number plates. Each character is recognized by an SVM, which is trained by some known samples in advance. In order to recognize a number plate correctly, all numbers are tested one by one using the trained model. The recognition results are achieved by finding the maximum value between the outputs of SVMs. In this paper, experimental results based on SVMs are given. From the experimental results, we can make the conclusion that SVM is better than others such as inductive learning-based number recognition

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006
DOIs
Publication statusPublished - 2006
Externally publishedYes
EventIEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006 - Sydney, NSW, Australia
Duration: 22 Nov 200624 Nov 2006

Publication series

NameProceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006

Conference

ConferenceIEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006
Country/TerritoryAustralia
CitySydney, NSW
Period22/11/0624/11/06

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

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