A Comparison of BPNN, RBF, and ENN in Number Plate Recognition

Chin, Kim On and Teo, Kein Yao and Rayner Alfred and Ag Asri Ag Ibrahim and Wang, Cheng and Tan, Tse Guan (2016) A Comparison of BPNN, RBF, and ENN in Number Plate Recognition. In: Second International Conference, SCDS 2016, 21-22 September 2016, Kuala Lumpur, Malaysia.

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Abstract

In this paper, we discuss a research project that related to autonomous recognition of Malaysia car plates using neural network approaches. This research aims to compare the proposed conventional Back propagation Feed Forward Neural Network (BPNN), Radial Basis Function Network (RBF), and Ensemble Neural Network (ENN). There are numerous research articles discussed the performances of BPNN and RFB in various applications. Interestingly, there is lack of discussion and application of ENN approach as the idea of ENN is still very young. Furthermore, this paper also discusses a novel technique used to localize car plate automatically without labelling them or matching their positions with template. The proposed method could solve most of the localization challenges. The experimental results show the proposed technique could automatically localize most of the car plate. The testing results show that the proposed ENN performed better than the BPNN and RBF. Furthermore, the proposed RBF performed better than BPNN.

Item Type:	Conference or Workshop Item (Paper)
Keyword:	Backpropagation Feed Forward Neural Network , BPNN , Radial Basis Function Network , RBF , Ensemble Neural Network , ENN , Car plate recognition , Image processing
Subjects:	T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	SAFRUDIN BIN DARUN -
Date Deposited:	29 Jul 2021 23:23
Last Modified:	29 Jul 2021 23:23
URI:	https://eprints.ums.edu.my/id/eprint/28932

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