International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Design of a Recognizing System for Vehicle's License Plates with English Characters

  • Xing, Xiong (School of Electronic Engineering, Daegu University) ;
  • Choi, Byung-Jae (School of Electronic Engineering, Daegu University) ;
  • Chae, Seog (School of Electronic Engineering, Kumoh University) ;
  • Lee, Mun-Hee (Daegu Gyeongbuk Development Institute)
  • Received : 2009.07.17
  • Accepted : 2009.09.10
  • Published : 2009.09.30
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Abstract

In recent years, video detection systems have been implemented in various infrastructures such as airport, public transportation, power generation system, water dam and so on. Recognizing moving objects in video sequence is an important problem in computer vision, with applications in several fields, such as video surveillance and target tracking. Segmentation and tracking of multiple vehicles in crowded situations is made difficult by inter-object occlusion. In the system described in this paper, the mean shift algorithm is firstly used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate or not. And then some characters in the license plate is recognized by using the fuzzy ARTMAP neural network, which is a relatively new architecture of the neural network family and has the capability to learn incrementally unlike the conventional BP network. We finally design a license plate recognition system using the mean shift algorithm and fuzzy ARTMAP neural network and show its performance via some computer simulations.

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References

  1. Chang Seok Park, Byeong Man Kim, Byung Hoon Seo, Jun Woo Kim, and Kwang Ho Lee., 4, "Real-time Recognition of Car License Plate on a Moving Car," Korea Society of Industrial Information System, vol. 9, no. 2, pp. 32-43, 2004
  2. Kwang-Baek Kim and Jae-Hyun Cho, "Recognition System of Car License Plate using Fuzzy Neural Networks," Korea Society of Computer Information System, vol. 12, no. 5, pp. 313-319, 2007
  3. Kwang-Baek Kim, Young Woon Woo and Choong-Shik Park, "Recognition of a New Car License Plate Using HSI Information, Fuzzy Binarization and ART2 Algorithm," International Journal of Maritime Information and Communication Sciences, vol. 11, no. 5, pp.1004-1012, 2007
  4. Hans A. Hegt, Ron J. De la Haye, and Nadeem A. Khan, "A High Performance License Plate Recognition System," IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 4357-4362, 1998
  5. J. R. Cowell, "Syntactic Pattern Recognizer for Vehicle Identification numbers," Image and Vision Computing, vol. 13, no. 1, pp. 13-19, 1995 https://doi.org/10.1016/0262-8856(95)91464-O
  6. Dorin Comaniciu and Peter Meer, "Mean Shift: A Robust Approach Toward Feature Space Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, 2002 https://doi.org/10.1109/34.1000236
  7. Gail A. Carpenter, Stephen Grossberg, Natalya Markuzon, John H. Reynolds, and David B. Bosen, "Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multi-dimensional Maps," IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 698-713 1992 https://doi.org/10.1109/72.159059

Cited by

  1. Vehicle license plate detection using region-based convolutional neural networks pp.1433-7479, 2017, https://doi.org/10.1007/s00500-017-2696-2