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License Plate Detection and Character Segmentation Using Adaptive Binarization Based on Superpixels under Illumination Change
Journal article   Open access   Peer reviewed

License Plate Detection and Character Segmentation Using Adaptive Binarization Based on Superpixels under Illumination Change

Daehun Kim, Bonhwa Ku, David K. Han and Hanseok Ko
IEICE transactions on information and systems, v E100D(6), pp 1384-1387
01 Jan 2017
DOI: https://doi.org/10.1587/transinf.2016EDL8206
url
https://doi.org/10.1587/transinf.2016EDL8206View
Published, Version of Record (VoR) Open

Abstract

Computer Science Computer Science, Information Systems Computer Science, Software Engineering Science & Technology Technology
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.

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2 citations in Scopus

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Domestic collaboration
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Web of Science research areas
Computer Science, Information Systems
Computer Science, Software Engineering
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