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Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates

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@article{IJASEIT1488,
   author = {Chin Kim On and Teo Kein Yau and Rayner Alfred and Jason Teo and Patricia Anthony and Wang Cheng},
   title = {Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {6},
   number = {6},
   year = {2016},
   pages = {1112--1119},
   keywords = {Car Plate Recognition; Image Processing; Ensemble Neural Network; Feed-forward Neural Network; Backpropagation.},
   abstract = {In this paper, we describe a research project that autonomously localizes and recognizes non-standardized Malaysian’s car plates using conventional Backpropagation algorithm (BPP) in combination with Ensemble Neural Network (ENN). We compared the results with the results obtained using simple Feed-Forward Neural Network (FFNN). This research aims to solve four main issues; (1) localization of car plates that has the same colour with the vehicle colour, (2) detection and recognition of car plates with varying sizes, (3) detection and recognition of car plates with different font types, and (4) detection and recognition of non-standardized car plates. The non-standardized Malaysian’s car plates are different from the normal plate as they contain italic characters, a combination of cursive characters, and different font types. The experimental results show that the combination of backpropagation and ENN can be effectively used to solve these four issues. The combination of BPP and ENN’s algorithm achieved a localization rate of 98% and a 97% in recognition rate. On the other hand, the combination of backpropagation and simple FFNN recorded a 96% recognition rate.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1488},
   doi = {10.18517/ijaseit.6.6.1488}
}

EndNote

%A Kim On, Chin
%A Kein Yau, Teo
%A Alfred, Rayner
%A Teo, Jason
%A Anthony, Patricia
%A Cheng, Wang
%D 2016
%T Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates
%B 2016
%9 Car Plate Recognition; Image Processing; Ensemble Neural Network; Feed-forward Neural Network; Backpropagation.
%! Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates
%K Car Plate Recognition; Image Processing; Ensemble Neural Network; Feed-forward Neural Network; Backpropagation.
%X In this paper, we describe a research project that autonomously localizes and recognizes non-standardized Malaysian’s car plates using conventional Backpropagation algorithm (BPP) in combination with Ensemble Neural Network (ENN). We compared the results with the results obtained using simple Feed-Forward Neural Network (FFNN). This research aims to solve four main issues; (1) localization of car plates that has the same colour with the vehicle colour, (2) detection and recognition of car plates with varying sizes, (3) detection and recognition of car plates with different font types, and (4) detection and recognition of non-standardized car plates. The non-standardized Malaysian’s car plates are different from the normal plate as they contain italic characters, a combination of cursive characters, and different font types. The experimental results show that the combination of backpropagation and ENN can be effectively used to solve these four issues. The combination of BPP and ENN’s algorithm achieved a localization rate of 98% and a 97% in recognition rate. On the other hand, the combination of backpropagation and simple FFNN recorded a 96% recognition rate.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1488
%R doi:10.18517/ijaseit.6.6.1488
%J International Journal on Advanced Science, Engineering and Information Technology
%V 6
%N 6
%@ 2088-5334

IEEE

Chin Kim On,Teo Kein Yau,Rayner Alfred,Jason Teo,Patricia Anthony and Wang Cheng,"Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates," International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 1112-1119, 2016. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.6.6.1488.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Kim On, Chin
AU  - Kein Yau, Teo
AU  - Alfred, Rayner
AU  - Teo, Jason
AU  - Anthony, Patricia
AU  - Cheng, Wang
PY  - 2016
TI  - Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 6 (2016) No. 6
Y2  - 2016
SP  - 1112
EP  - 1119
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Car Plate Recognition; Image Processing; Ensemble Neural Network; Feed-forward Neural Network; Backpropagation.
N2  - In this paper, we describe a research project that autonomously localizes and recognizes non-standardized Malaysian’s car plates using conventional Backpropagation algorithm (BPP) in combination with Ensemble Neural Network (ENN). We compared the results with the results obtained using simple Feed-Forward Neural Network (FFNN). This research aims to solve four main issues; (1) localization of car plates that has the same colour with the vehicle colour, (2) detection and recognition of car plates with varying sizes, (3) detection and recognition of car plates with different font types, and (4) detection and recognition of non-standardized car plates. The non-standardized Malaysian’s car plates are different from the normal plate as they contain italic characters, a combination of cursive characters, and different font types. The experimental results show that the combination of backpropagation and ENN can be effectively used to solve these four issues. The combination of BPP and ENN’s algorithm achieved a localization rate of 98% and a 97% in recognition rate. On the other hand, the combination of backpropagation and simple FFNN recorded a 96% recognition rate.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1488
DO  - 10.18517/ijaseit.6.6.1488

RefWorks

RT Journal Article
ID 1488
A1 Kim On, Chin
A1 Kein Yau, Teo
A1 Alfred, Rayner
A1 Teo, Jason
A1 Anthony, Patricia
A1 Cheng, Wang
T1 Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates
JF International Journal on Advanced Science, Engineering and Information Technology
VO 6
IS 6
YR 2016
SP 1112
OP 1119
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Car Plate Recognition; Image Processing; Ensemble Neural Network; Feed-forward Neural Network; Backpropagation.
AB In this paper, we describe a research project that autonomously localizes and recognizes non-standardized Malaysian’s car plates using conventional Backpropagation algorithm (BPP) in combination with Ensemble Neural Network (ENN). We compared the results with the results obtained using simple Feed-Forward Neural Network (FFNN). This research aims to solve four main issues; (1) localization of car plates that has the same colour with the vehicle colour, (2) detection and recognition of car plates with varying sizes, (3) detection and recognition of car plates with different font types, and (4) detection and recognition of non-standardized car plates. The non-standardized Malaysian’s car plates are different from the normal plate as they contain italic characters, a combination of cursive characters, and different font types. The experimental results show that the combination of backpropagation and ENN can be effectively used to solve these four issues. The combination of BPP and ENN’s algorithm achieved a localization rate of 98% and a 97% in recognition rate. On the other hand, the combination of backpropagation and simple FFNN recorded a 96% recognition rate.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1488
DO  - 10.18517/ijaseit.6.6.1488