Developing Recognition System for New Iraqi License Plate
Article Sidebar
Main Article Content
Abstract
Because of rapid population growth and increasing need for humans to use the vehicles in the last decade. Identifying these vehicles by license plates is considered. The quick development in image processing field can solve this issue by capturing the image of these vehicles and then identify the vehicle license plate. Most traffic applications depend on Automatic detection of the license plate and identification technology such as searching for stolen vehicles, traffic control on the road, monitoring cars from entering area, some information about the vehicle, parking systems, monitor the border crossing, maximum speed or red-light violation ticket, and identify the identity of the driver etc. In this paper we try to design a system that can identify the characters of the new plates of vehicles in Iraq, which relies on image recognition. First capture the picture of the vehicle using a digital camera and recognize the text of the captured pictures using optical character recognition technology and then compared it with all vehicle plates numbers stored in the database.
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY LICENSE http://creativecommons.org/licenses/by/4.0/
Plaudit
References
Christos NE. Anagnostopoulos, IE. Anagnostop-
oulos, VL, Eleftherios K. A license plate-recognition algorithm for intelligent transportation system applications. Browse Journals & Magazines 2006; 7 (3): 377-392. DOI: https://doi.org/10.1109/TITS.2006.880641
Öztürk F, Özen F. A new license plate recognition system based on probabilistic neural networks. Procedia Technology 2012; 1: 124-128. DOI: https://doi.org/10.1016/j.protcy.2012.02.024
Roy A, Ghoshal DP. Number plate recognition for use in different countries using an improved segmenation. IEEE in 2nd National Conference on Emerging Trends and Applications in Computer Science (NCETACS) 2011, 4-5 March; India: pp. 1-5. DOI: https://doi.org/10.1109/NCETACS.2011.5751407
Chen Z-X, Liu C-Y, Chang F-L, Wang G-Y. Automatic license-plate location and recognition based on feature salience. IEEE Transactions on Vehicular Technology 2009; l (7): 3781-3785. DOI: https://doi.org/10.1109/TVT.2009.2013139
Wen Y et al. An algorithm for license plate recognition applied to intelligent transportation system. IEEE Transactions of Intelligent Transportation Systems 2011; 12 (3): 1-16. DOI: https://doi.org/10.1109/TITS.2011.2114346
Jia W, Zhang H, He X. Region-based license plate detection. Journal of Network and Computer Applications 2007; 30 (4): 1324-1333. DOI: https://doi.org/10.1016/j.jnca.2006.09.010
Yang Y, Gao X, Yang G. Study the method of vehicle license locating based on color segmentation. Procedia Engineering 2011; 15: 1324-1329. DOI: https://doi.org/10.1016/j.proeng.2011.08.245
Wang F et al. Fuzzy-based algorithm for color recognition of license plates. Pattern Recognition Letters 2008; 29 (7): 1007-1020. DOI: https://doi.org/10.1016/j.patrec.2008.01.026
Kamal NN. Iraqi license plate recognition system: Baghdad University, Baghdad, Iraq: 2013.
Jones, PV. Robust real-time object detection. International Journal of Computer Vision 2004; 57 (2): 137–154. DOI: https://doi.org/10.1023/B:VISI.0000013087.49260.fb
Paul V, Michael J. Rapid object detection using a boosted cascade of simple features. IEEE Conference on Computer Vision and Pattern Recognition 2001, December.