JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Number Plate Detection with a Multi-Convolutional Neural Network Approach with Optical Character Recognition for Mobile Devices
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Number Plate Detection with a Multi-Convolutional Neural Network Approach with Optical Character Recognition for Mobile Devices
Gerber, Christian; Chung, Mokdong;
  PDF(new window)
 Abstract
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.
 Keywords
Convolutional Neural Network;Number Plate Detection;OCR;
 Language
English
 Cited by
 References
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.

2.
J. Li, C. Niu, and M. Fan, "Multi-scale convolutional neural networks for natural scene license plate detection," in Advances in Neural Networks-ISNN 2012. Heidelberg: Springer, 2012, pp. 110-119.

3.
I. J. Goodfellow, Y. Bulatov, J. Ibarz, S. Arnoud, and V. Shet, "Multi-digit number recognition from street view imagery using deep convolutional neural networks," Apr. 2014; http://arxiv.org/pdf/1312.6082v4.pdf.

4.
L. M. Belue and K. W. Bauer, "Determining input features for multilayer perceptrons," Neurocomputing, vol. 7, no. 2, pp. 111-121, 1995. crossref(new window)

5.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.

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/.