Genetic Algorithm Based Neural Network for License Plate Recognition

  • Wang Xiaobin
  • Li Hao
  • Wu Lijuan
  • Hong Qu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7951)


This paper combines genetic algorithms and neural networks to recognize vehicle license plate characters. We train the neural networks using a genetic algorithm to find optimal weights and thresholds. The traditional genetic algorithm is improved by using a real number encoding method to enhance the networks weight and threshold accuracy. At the same time, we use a variety of crossover operations in parallel, which broadens the range of the species and helps the search for the global optimal solution. An adaptive mutation rate both ensures the diversity of the species and makes the algorithm convergence more rapidly to the global optimum. Experiments show that this method greatly improves learning efficiency and convergence speed.


license plate recognition genetic algorithms neural networks character recognition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chen, Z.-X., Liu, C.-Y., Chang, F.-L., Wang, G.-Y.: Automatic License-Plate Location and Recognition Based on Feature Salience. Vehicular Technology 58(7), 3781–3785 (2009)CrossRefGoogle Scholar
  2. 2.
    Ying, W., Yue, L., Jingqi, Y., Zhenyu, Z., von Deneen, K.M., Pengfei, S.: An Algorithm for License Plate Recognition Applied to Intelligent Transportation System. Intelligent Transportation Systems 12(3), 830–845 (2011)Google Scholar
  3. 3.
    Xiaoping, L., Xiaoxing, L., Shuaizong, W., Lin, Z., Yinxiang, L., Hongjian, D.: Research on the Recognition Algorithm of the License Plate Character Based on the Multi-resolution Template Matching. In: 4th International Conference on New Trends in Information Science and Service Science, pp. 341–344. IEEE Press, Gyeongju South Korea (2010)Google Scholar
  4. 4.
    Wakahara, T., Yamashita, Y.: Multi-template GAT/PAT Correlation for Character Recognition with a Limited Quantity of Data. In: 20th International Conference on Pattern Recognition, pp. 2873–2876. IEEE Press, Istanbul Turkey (2010)Google Scholar
  5. 5.
    Jianlan, F., Yuping, L., Mianzhou, C.: The research of vehicle license plate character recognition method based on artificial neural network. In: 2nd International Asia Conference on Informatics in Control, Automation and Robotics, pp. 317–320. IEEE Press, Wuhan China (2010)Google Scholar
  6. 6.
    Jianwei, G., Jinguang, S.: License Plate Recognition System Based on Orthometric Hopfield Network. In: International Conference on MultiMedia and Information Technology, MMIT 2008, pp. 594–597. IEEE Press, Three Gorges China (2008)Google Scholar
  7. 7.
    Abdullah, S.N.H.S., Omar, K., Sahran, S., Khalid, M.: License Plate Recognition Based on Support Vector Machine. In: International Conference on Electrical Engineering and Informatics, ICEEI 2009, pp. 78–82. IEEE Press, Selangor Malaysia (2009)CrossRefGoogle Scholar
  8. 8.
    Malheiros-Silveira, G.N., Hernandez-Figueroa, H.E.: Prediction of Dispersion Relation and PBGs in 2-D PCs by Using Artificial Neural Networks. Photonics Technology Letters 24(20), 1799–1801 (2012)CrossRefGoogle Scholar
  9. 9.
    Domenech, C., Wehr, T.: Use of Artificial Neural Networks to Retrieve TOA SW Radiative Fluxes for the EarthCARE Mission. Geoscience and Remote Sensing 49(6), 1839–1849 (2011)CrossRefGoogle Scholar
  10. 10.
    Janakiraman, V., Bharadwaj, A., Visvanathan, V.: Voltage and Temperature Aware Statistical Leakage Analysis Framework Using Artificial Neural Networks. Computer-Aided Design of Integrated Circuits and Systems 29(7), 1056–1069 (2011)CrossRefGoogle Scholar
  11. 11.
    Zhou, D.Q., Annakkage, U.D., Rajapakse, A.D.: Online Monitoring of Voltage Stability Margin Using an Artificial Neural Network. Power Systems 25(3), 1566–1574 (2010)CrossRefGoogle Scholar
  12. 12.
    Zuqing, Z., Chuanqi, W., Weida, Z.: Using Genetic Algorithm to Optimize Mixed Placement of 1R/2R/3R Regenerators in Translucent Lightpaths for Energy-Efficient Design. Communications Letters 16(2), 262–264 (2012)CrossRefGoogle Scholar
  13. 13.
    Lau, H.C.W., Chan, T.M., Tsui, W.T., Pang, W.K.: Application of Genetic Algorithms to Solve the Multidepot Vehicle Routing Problem. Automation Science and Engineering 7(2), 382–392 (2010)Google Scholar
  14. 14.
    Cabral, H.A., de Melo, M.T.: Using Genetic Algorithms for Device Modeling. Magnetics 47(5), 1322–1325 (2011)CrossRefGoogle Scholar
  15. 15.
    Shuo, C., Arnold, D.F.: Optimization of Permanent Magnet Assemblies Using Genetic Algorithms. Magnetics 47(10), 4104–4107 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wang Xiaobin
    • 1
  • Li Hao
    • 1
  • Wu Lijuan
    • 1
  • Hong Qu
    • 1
  1. 1.Computational Intelligence Laboratory, School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChina

Personalised recommendations