License plate detection and character segmentation using adaptive binarization based on superpixels under illumination change

Daehun Kim, Bonhwa Ku, David K. Han, Hanseok Ko

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In this paper, an algorithm is proposed for license plate recognition (LPR) in video traffic surveillance applications. In an LPR system, the primary steps are license plate detection and character segmentation. However, in practice, false alarms often occur due to images of vehicle parts that are similar in appearance to a license plate or detection rate degradation due to local illumination changes. To alleviate these difficulties, the proposed license plate segmentation employs an adaptive binarization using a superpixel-based local contrast measurement. From the binarization, we apply a set of rules to a sequence of characters in a sub-image region to determine whether it is part of a license plate. This process is effective in reducing false alarms and improving detection rates. Our experimental results demonstrate a significant improvement over conventional methods.

Original languageEnglish
Pages (from-to)1384-1387
Number of pages4
JournalIEICE Transactions on Information and Systems
VolumeE100D
Issue number6
DOIs
Publication statusPublished - 2017 Jun 1

Keywords

  • Binarization
  • License plate character segmentation
  • License plate detection
  • Superpixel algorithm

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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