Fuzzy logic based vehicular plate character recognition system using image segmentation and scale-invariant feature transform

This paper proposes a vehicle plate optical character recognition method using scale invariant feature transform integrated with image segmentation and fuzzy logic. Image segmentation separates every character in a plate area to get the features of every character obtained. Scale Invariant Feature Transform or SIFT on the other hand, allows the extraction of every feature of each character obtained from the plate. Fuzzy logic analyzes the features obtained from the SIFT algorithm which is proposed to detect the characters correctly. This program used MATLAB to determine the performance of the algorithm. Using the proposed algorithm, it was shown how the algorithm was effective on extracting plate character features as well as recognizing the characters in a given image. Results show that the algorithm has an accuracy of 90.75% and now ready to use for other implementation. This can be incorporated to present optical character recognition system and test its validity and accuracy for practical purposes.

[1]  Jun Tang,et al.  A color image segmentation algorithm based on region growing , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[2]  Aram Kawewong,et al.  License plate localization using MSERs and vehicle frontal mask localization using visual saliency for vehicle recognition , 2014, 2014 Fourth International Conference on Digital Information and Communication Technology and its Applications (DICTAP).

[3]  Pletl Szilveszter,et al.  Parking surveillance and number plate recognition application , 2010, IEEE 8th International Symposium on Intelligent Systems and Informatics.

[4]  Jing-Ming Guo,et al.  License Plate Localization and Character Segmentation With Feedback Self-Learning and Hybrid Binarization Techniques , 2008, IEEE Transactions on Vehicular Technology.

[5]  Priyanto Hidayatullah,et al.  Optical Character Recognition Improvement for License Plate Recognition in Indonesia , 2012, 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation.

[6]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[7]  Kamarul Hawari,et al.  Development of automatic vehicle plate detection system , 2013, 2013 IEEE 3rd International Conference on System Engineering and Technology.

[8]  Argel A. Bandala,et al.  Object recognition and detection by shape and color pattern recognition utilizing Artificial Neural Networks , 2013, 2013 International Conference of Information and Communication Technology (ICoICT).

[9]  Xiaojun Zhai,et al.  OCR-based neural network for ANPR , 2012, 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings.

[10]  G. Abo Samra,et al.  Localization of License Plate Number Using Dynamic Image Processing Techniques and Genetic Algorithms , 2014, IEEE Transactions on Evolutionary Computation.

[11]  J. Mendel.  Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[12]  Kuo-Chin Fan,et al.  Vehicle licence plate recognition using super-resolution technique , 2014, 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[13]  K Arulmozhi,et al.  Application of Top Hat Transform technique on Indian license plate image localization , 2012, 2012 IEEE International Conference on Computational Intelligence and Computing Research.

[14]  Rhen Anjerome Bedruz,et al.  Comparison of Huffman Algorithm and Lempel-Ziv Algorithm for audio, image and text compression , 2015, 2015 International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM).

[15]  Abraham Kandel,et al.  Complex fuzzy logic , 2003, IEEE Trans. Fuzzy Syst..

[16]  L. S. Bartolome,et al.  Vehicle parking inventory system utilizing image recognition through artificial neural networks , 2012, TENCON 2012 IEEE Region 10 Conference.

[17]  Elmer P. Dadios,et al.  Machine vision for traffic violation detection system through genetic algorithm , 2015, 2015 International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM).

[18]  Elmer P. Dadios,et al.  A genetic algorithm and artificial neural network-based approach for the machine vision of plate segmentation and character recognition , 2015, 2015 International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM).