Region-based license plate detection

Wenjing Jia, Huaifeng Zhang, Xiangjian He

Research output: Journal PublicationArticlepeer-review

112 Citations (Scopus)

Abstract

Automatic license plate recognition (ALPR) is one of the most important aspects of applying computer techniques towards intelligent transportation systems. In order to recognize a license plate efficiently, however, the location of the license plate, in most cases, must be detected in the first place. Due to this reason, detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of an ALPR system, which greatly affects the recognition rate and speed of the whole system. In this paper, a region-based license plate detection method is proposed. In this method, firstly, mean shift is used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate. Unlike other existing license plate detection methods, the proposed method focuses on regions, which demonstrates to be more robust to interference characters and more accurate when compared with other methods.

Original languageEnglish
Pages (from-to)1324-1333
Number of pages10
JournalJournal of Network and Computer Applications
Volume30
Issue number4
DOIs
Publication statusPublished - Nov 2007
Externally publishedYes

Keywords

  • Features
  • License plate detection
  • Mahalanobis classifier
  • Mean-shift segmentation
  • Region

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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