Abstract
License Plate Recognition is a practical use of computer vision based application. With the increase in demand of automation transportation systems, this application plays a very big role in the system development. Also, the use of vehicles has been increasing because of population growth and human needs in recent years makes the application is more challenging. Moreover, license plates are available in diverse colors and style and that the presence of noise, blurring in the image, uneven illumination, and occlusion makes the task even more difficult for conventional recognition methods. We propose an approach of using a Convolutional Neural Networks (CNN) classifier for the recognition. Pre-processing techniques are firstly applied on input images, such as filtering, thresholding, and then segmentation. Then, we train a CNN classifier for character recognition. Although the performance of a CNN is very impressive, it costs much time to complete the character recognition step. In this study, a modified CNN is proposed to help the system run in real-time. Experimental results have done and analyzed with other methods.
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References
Ahmed, M.J., Sarfraz, M., Zidouri, A., Al-Khatib, W.G.: License plate recognition system. In: Proceedings of the 2003 10th IEEE International Conference on Electronics, Circuits and Systems, 2003, ICECS 2003, pp. 898–901. IEEE (2003)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
Siddharth, K.S., Jangid, M., Dhir, R., Rani, R.: Handwritten Gurmukhi character recognition using statistical and background directional distribution. Int. J. Comput. Sci. Eng. (IJCSE) 3, 2332–2345 (2011)
Sharma, S., Singh, N.: Optical character recognition using artificial neural networks approach. Int. J. Emerg. Technol. Adv. Eng. 4, 339–344 (2014)
Bouchain, D.: Character recognition using convolutional neural networks. Inst. Neural Inf. Process. 2007 (2006)
Zheng, D., Zhao, Y., Wang, J.: An efficient method of license plate location. Pattern Recogn. Lett. 26, 2431–2438 (2005)
Wen, Y., Lu, Y., Yan, J., Zhou, Z., von Deneen, K.M., Shi, P.: An algorithm for license plate recognition applied to intelligent transportation system. IEEE Trans. Intell. Transp. Syst. 12, 830–845 (2011)
Caner, H., Gecim, H.S., Alkar, A.Z.: Efficient embedded neural-network-based license plate recognition system. IEEE Trans. Veh. Technol. 57, 2675–2683 (2008)
Anagnostopoulos, C.N.E., Anagnostopoulos, I.E., Loumos, V., Kayafas, E.: A license plate-recognition algorithm for intelligent transportation system applications. IEEE Trans. Intell. Transp. Syst. 7, 377–392 (2006)
Jin, L., Xian, H., Bie, J., Sun, Y., Hou, H., Niu, Q.: License plate recognition algorithm for passenger cars in Chinese residential areas. Sensors 12, 8355–8370 (2012)
Kumari, S., Gupta, D., Singh, R.M.: A robust method for vehicle license plate recognition based on Harries corner algorithm and artificial neural network. Int. J. Comput. Appl. 148, 16–19 (2016)
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Pham, V.H., Dinh, P.Q., Nguyen, V.H. (2018). CNN-Based Character Recognition for License Plate Recognition System. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10752. Springer, Cham. https://doi.org/10.1007/978-3-319-75420-8_56
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DOI: https://doi.org/10.1007/978-3-319-75420-8_56
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