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License plate recognition algorithm on the basis of a connected components method and a hierarchical temporal memory model
Yu.A. Bolotova, V.G. Spitsyn, M.N. Rudometkina

 

Tomsk Polytechnic University

 

DOI: 10.18287/0134-2452-2015-39-2-275-280

Full text of article: Russian language.

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Abstract:
This paper proposes a license plate recognition algorithm that consists of three major steps: image preprocessing, segmentation, and recognition, which works efficiently with day- and nighttime images, as well as with the license plate being tilted.
Pre-filtration allows the sequential binarization to be conducted efficiently. Typically, the license plate segmentation is realized by a histogram method with the preliminary plate de-rotation to the horizontal position, thus deteriorating the original image quality. In this paper the segmentation is implemented by a connected components method, enabling the rotation and a consequent loss of quality to be avoided. The hierarchical temporal network shows good results in rotated symbols recognition. The proposed method can be used in a similar way for segmentation and recognition of various text data. The proposed algorithms can also be used for distorted text segmentation and recognition.

Keywords:
hierarchical temporal memory, temporal grouping, license plate detection.

Citation:
Bolotova YA, Spitsyn VG, Rudometkina MN. License plate recognition algorithm on the basis of a connected components method and a hierarchical temporal memory model. Computer Optics 2015; 39(2): 275-280. DOI: 10.18287/0134-2452-2015-39-2-275-280.

References:

  1. Jia, W.J. Region-based license plate detection / W.J. Jia, H.F. Zhang, X.J. He // Journal of Network and Computer Applications. – 2007. – Vol. 30(4). – P. 1324-1333.
  2. Zheng, L.H. Accuracy enhancement for license plate recognition / L.H. Zheng, X.J. He, B. Samali, L.T. Yang // Proceedings of the International Conference on Computer and Information Technology. – 2010. – P. 511-615.
  3. Han, C.C. License plate detection and recognition using a dual-camera module in a large space / C.C. Han, C.T. Hsieh, Y.N. Chen, G.F. Ho, K.C. Fan, C.L. Tsai // 41st Annual IEEE International Carnahan Conference on Security Technology. – 2007. – P. 307-312.
  4. Glumov, N.I. Method of rapid correlation using ternary templates for object recognition in images / N.I. Glumov, E.V. Mjasnikov, V.N. Kopenkov, M.A. Chicheva // Computer Optics. – 2008. – Vol. 32(3). – P. 277-282. – (In Russian).
  5. Caner, H. Efficient embedded neural network-based license plate recognition system / H. Caner, H.S. Gecim, A.Z. Alkar // IEEE Transactions on Vehicular Technology. – 2008. – Vol. 57(5). – P. 2675-2683.
  6. Park, S.H. Locating car license plate using neural networks / S.H. Park, K.I. Kim, K. Jung, H.J. Kim // Electronics Letters. – 1999. – Vol. 35(17). – P. 1475-1477.
  7. Otsu, N. A threshold selection method from gray-scale histogram / N. Otsu // IEEE Transactions on System, Man, and Cybernetics. – 1979. – P. 62-66.
  8. Sterganov, M. Methodology of allocation the connected components in binary barcode images / M. Sterganov // Proceedings 20th International Conference on Computer Graphics and Vision. – 2010. – P. 169-174. – (In Russian).
  9. Hawkins, J. Hierarchical temporal memory concepts, theory and terminology [Electronical Resource] / J. Hawkins, D. George. – URL: http://www.nimenta.com/htm-overvi­ew/education /Numenta_HTM_concepts.pdf (request date 19.11.2014).
  10. Bolotova, Yu.A. Analysis of hierarchically-temporal dependencies for handwritten symbols and gesture recognition / Yu.A. Bolotova, V.G. Spitsyn // Proceedings of 7-th International Forum on Strategic Technology (IFOST). – 2012. – P. 1-6.
  11. Shridhar, M. License plate recognition using SKIPSM / M. Shridhar, F.M. Waltz, J.W.V. Miller, G. Houle, L. Bij­nagte, R. Dibble // Proceedings of SPIE. The International society for Optical Engineering. – 2001. – Vol. 4189. – P. 72-79.
  12. Kennady, C.N. A feature based approach for license plate recognition of Indian number plates / C.N. Kennady, S.T. Subramanian, K. Parasuraman // IEEE International Conference on Computational Intelligence and Computing Research. – 2010. – P. 1-4.
  13. Dehshibi, M.M. Persian Vehicle License Plate Recognition Using Multiclass AdaBoost / M.M. Dehshibi, R. Allahverdi // International Journal of Computer & Electrical Engineering. – 2012. – Vol. 4(3). – P. 355.
  14. Chang, S.-L. Automatic license plate recognition / S.-L. Chang, L.-S. Chen, Y.-Ch. Chung, S.-W. Chen // IEEE Transactions on Intelligent Transportation Systems. – 2004. – Vol. 5(1). – P. 42-53.
  15. Sun, G. A new recognition method of vehicle license plate base on genetic neural network / G. Sun, C. Zhang, W. Zou, G. Yu // 5th IEEE Conference on Industrial Electronics and Applications. – 2010. – P. 1662-1666.
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