Journal of Information Processing Systems

  • 제12권1호
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  • Pages.100-108
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  • 2016
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Number Plate Detection with a Multi-Convolutional Neural Network Approach with Optical Character Recognition for Mobile Devices

  • 투고 : 2015.04.13
  • 심사 : 2015.08.05
  • 발행 : 2016.03.31
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초록

In this paper, we propose a method to achieve improved number plate detection for mobile devices by applying a multiple convolutional neural network (CNN) approach. First, we processed supervised CNN-verified car detection and then we applied the detected car regions to the next supervised CNN-verifier for number plate detection. In the final step, the detected number plate regions were verified through optical character recognition by another CNN-verifier. Since mobile devices are limited in computation power, we are proposing a fast method to recognize number plates. We expect for it to be used in the field of intelligent transportation systems.

키워드

참고문헌

  1. Y. N. Chen, C. C. Han, C. T. Wang, B. S. Jeng, and K. C. Fan, "The application of a convolution neural network on face and license plate detection," in Proceedings of 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong, 2006, pp. 552-555.
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  6. Apple Inc., Part of iOS 6 Developer Library.
  7. C. Gerber and M. Chung, "Two-step convolutional neural network approach for improved number plate localization on iOS," in Proceedings of 2014 Korea Computer Congress (KCC2014), Busan, Korea, 2014, pp. 868- 870.
  8. California Institute of Technology, "Image_Datasets," 2015; http://www.vision.caltech.edu/Image_Datasets/.

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  1. Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks vol.22, pp.20, 2018, https://doi.org/10.1007/s00500-018-3256-0