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 journalArticlepeer-review

    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

    Keywords

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

    ASJC Scopus subject areas

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

    Fingerprint

    Dive into the research topics of 'License plate detection and character segmentation using adaptive binarization based on superpixels under illumination change'. Together they form a unique fingerprint.

    Cite this