License Plate Detection and Character Segmentation Using Adaptive Binarization Based on Superpixels under Illumination Change
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information 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
Authors
Daehun KIM
Korea University
Bonhwa KU
Korea University
David K. HAN
Office of Naval Research
Hanseok KO
Korea University
Keyword
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Daehun KIM, Bonhwa KU, David K. HAN, Hanseok KO, "License Plate Detection and Character Segmentation Using Adaptive Binarization Based on Superpixels under Illumination Change" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 6, pp. 1384-1387, June 2017, doi: 10.1587/transinf.2016EDL8206.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDL8206/_p
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@ARTICLE{e100-d_6_1384,
author={Daehun KIM, Bonhwa KU, David K. HAN, Hanseok KO, },
journal={IEICE TRANSACTIONS on Information},
title={License Plate Detection and Character Segmentation Using Adaptive Binarization Based on Superpixels under Illumination Change},
year={2017},
volume={E100-D},
number={6},
pages={1384-1387},
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.},
keywords={},
doi={10.1587/transinf.2016EDL8206},
ISSN={1745-1361},
month={June},}
Copy
TY - JOUR
TI - License Plate Detection and Character Segmentation Using Adaptive Binarization Based on Superpixels under Illumination Change
T2 - IEICE TRANSACTIONS on Information
SP - 1384
EP - 1387
AU - Daehun KIM
AU - Bonhwa KU
AU - David K. HAN
AU - Hanseok KO
PY - 2017
DO - 10.1587/transinf.2016EDL8206
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2017
AB - 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.
ER -
English
