THE AUTOMATIC LICENSE PLATE RECOGNITION USING FEATURES EXTRACTION AND NEURAL NETWORKS

  • REEM Q. ABDULJABBER
  • MOHAMMED A. SHAKIR
Keywords: Automatic license plate recognition system; Car plate detection; Normal cross correlation; Morphological operation; Image segmentation; Back propagation neural network.

Abstract

The automatic license plate recognition (ALPR) system opens the trendy door to the researchers to
think, discover techniques and reach to a result for its necessity. The important of the ALPR system is
appeared in the transportation for many reasons such as parking, traffic violations and security. The aim
of this paper is to suggest a scheme that will extract car number, country and province from the car
images. The proposed scheme is based on digital image processing techniques and neural networks. The
proposed algorithm is composite of preprocessing and recognition stages. The preprocessing stage includes:
locate the car plate region, binarization, enhancement of the image quality, segment the image into the
sub-images. The recognition stage will classify and recognize the segmented sub-images as numbers and
characters. In this research, the localization is done through normal cross correlation method. The
segmentation includes: segment the car plate into three regions, divide the number and separated
character into individual and split the connected characters into separated characters are done through
suggested algorithms. The recognition is accomplished using the back propagation neural network
(BPNN). The recognizer operates on two sets of data. First set of data includes the whole pixels of the
sub-images. The second set of data is based on 16 features extracted from the sub-images. A comparison
between these two methods is made. The system is experienced on 99 images of Duhok and Erbil provinces,
the environment work is done with MATLAB program. The percentage accuracy is: 100%,100% and 100%
for the localization, distinguish and segmentation respectively. The recognition rate result for the first
method is 94.5% and the second method is 91%.

Downloads

Download data is not yet available.

Author Biographies

REEM Q. ABDULJABBER
Dept. of Electrical and computer Engineering, College of Engineering, University of Duhok , Kurdistan Region - Iraq
MOHAMMED A. SHAKIR
Dept. of Electrical and computer Engineering, College of Engineering, University of Duhok , Kurdistan Region - Iraq

References

Jain, A. S., & Kundargi, J. M. (2015). Automatic
number plate recognition using artificial
neural network. Int. Res. J. Eng. Technol, 2(4),
633-639.
Wang, J., & Yan, W. Q. (2016). BP-Neural Network
for Plate Number Recognition. International Journal of Digital Crime and Forensics
(IJDCF), 8(3), 34-45.
Omran, S. S., & Jarallah, J. A. (2017, December).
Iraqi License Plate Localization and
Recognition System Using Neural Network.
In 2017 Second Al-Sadiq International
Conference on Multidisciplinary in IT and
Communication Science and Applications
(AIC-MITCSA) (pp. 73-78). IEEE.
Sharma, G. (2018). Performance analysis of vehicle
number plate recognition system using
template matching techniques. Journal of
Information Technology & Software
Engineering, 8(2), 10-4172.
Indira, K., Mohan, K. V., & Nikhilashwary, T. (2019).
Automatic license plate recognition. In Recent
Trends in Signal and Image Processing (pp.
67-77). Springer, Singapore.
Stastny, Jiri & Minařík, Martin (2007). A Brief
Introduction to Image Pre- Processing for
Object Recognition.
IMAQ, N. (2000). IMAQ Vision Concepts
Manual. National Instruments.
Jain, R., Kasturi, R., & Schunck, B. G.
(1995). Machine vision (Vol. 5, pp. 309-364).
New York: McGraw-hill.
Yousefi, J. (2011). Image binarization using Otsu
thresholding algorithm. University of Guelph,
Ontario, Canada.
Boyat, A. K., & Joshi, B. K. (2015). A review paper:
noise models in digital image
processing. arXiv preprint arXiv:1505.03489.
Solomon, C., & Breckon, T. (2011). Fundamentals of
Digital Image Processing: A practical
approach with examples in Matlab. John
Wiley & Sons.
Vithlani, P., & Kumbharana, C. K. (2015). Structural
and statistical feature extraction methods for
character and digit recognition. International
Journal of Computer Applications, 120(24),
0975-8887.
Omidiora, E. O., Oladele, M. O., Adepoju, T. M.,
Sobowale, A. A., & Olatoke, O. A. (2016).
Comparative analysis of back propagation
neural network and self-organizing feature
map in estimating age groups using facial
features. Current Journal of Applied Science
and Technology, 1-7.
Cilimkovic, M. (2015). Neural networks and back
propagation algorithm. Institute of Technology
Blanchardstown, Blanchardstown Road North
Dublin, 15.
Anand, A. (2017). Unit-14 Accuracy Assessment.
IGNOU.
Published
2021-01-05
How to Cite
ABDULJABBER, R. Q., & SHAKIR, M. A. (2021). THE AUTOMATIC LICENSE PLATE RECOGNITION USING FEATURES EXTRACTION AND NEURAL NETWORKS. Journal of Duhok University, 23(2), 439-453. https://doi.org/10.26682/csjuod.2020.23.2.36