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Authors: Siti Norul Huda Sheikh Abdullah 1 ; Marzuki Khalid 1 ; Rubiyah Yusof 1 and Khairuddin Omar 2

Affiliations: 1 Centre for Artificial Intelligence and Robotics (CAIRO), Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Malaysia ; 2 Jabatan Sains dan Pengurusan Sistem, Fakulti Teknologi Sains Maklumat, Universiti Kebangsaan Malaysia, Malaysia

ISBN: 978-972-8865-59-7

Keyword(s): License plate recognition, clustering, run length smoothing algorithm, thresholding.

Related Ontology Subjects/Areas/Topics: Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Intelligent Fault Detection and Identification ; Neural Networks Based Control Systems

Abstract: Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates based on image processing, clustering, feature extraction and neural networks. The image processing library is developed in-house which referred to as Vision System Development Platform (VSDP).After applying image enhancement, the image is segmented using blob analysis, horizontal scan line profiles, clustering and run length smoothing algorithm approach to identify the location of the license plate. Thoroughly each image is transformed into blob objects and its important information such as total of blobs, location, height and width, are being analyzed for the purpose of cluster exercising and choosing the best cluster with winner blobs. Here, new algorithm called Cluster Run Length Smoothing Algorithm (CLSA) approach was applied to locate the license plate at the right position. CLSA consisted of two separate new proposed algorithm which applied new edge detector algorithm using 3x3 kernel masks and 128 grayscale offset plus a new way (3D method) to calculate run length smoothing algorithm (RLSA), which can improve clustering techniques in segmentation phase. Two separate experiments were performed; Cluster and Threshold value 130 (CT130) and CRLSA with Threshold value 1 (CCT1). The prototyped system has an accuracy more than 96% and suggestions to further improve te system are discussed in this paper pertaining to analysis of the error. (More)

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Paper citation in several formats:
Norul Huda Sheikh Abdullah S.; Khalid M.; Yusof R.; Omar K. and (2006). DETECTING LICENSE PLATE USING CLUSTER RUN LENGTH SMOOTHING ALGORITHM.In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO, ISBN 978-972-8865-59-7, pages 175-178. DOI: 10.5220/0001198501750178

@conference{icinco06,
author={Siti {Norul Huda Sheikh Abdullah} and Marzuki Khalid and Rubiyah Yusof and Khairuddin Omar},
title={DETECTING LICENSE PLATE USING CLUSTER RUN LENGTH SMOOTHING ALGORITHM},
booktitle={Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,},
year={2006},
pages={175-178},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001198501750178},
isbn={978-972-8865-59-7},
}

TY - CONF

JO - Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,
TI - DETECTING LICENSE PLATE USING CLUSTER RUN LENGTH SMOOTHING ALGORITHM
SN - 978-972-8865-59-7
AU - Norul Huda Sheikh Abdullah, S.
AU - Khalid, M.
AU - Yusof, R.
AU - Omar, K.
PY - 2006
SP - 175
EP - 178
DO - 10.5220/0001198501750178

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