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

Daehun KIM
Bonhwa KU
David K. HAN
Hanseok KO

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D    No.6    pp.1384-1387
Publication Date: 2017/06/01
Publicized: 2017/02/22
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDL8206
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
license plate detection,  license plate character segmentation,  binarization,  superpixel algorithm,  

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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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