[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3745/JIPS.04.0022

Number Plate Detection with a Multi-Convolutional Neural Network Approach with Optical Character Recognition for Mobile Devices

Gerber, Christian (Dept. of Computer Engineering, Pukyong National University)
Chung, Mokdong (Dept. of Computer Engineering, Pukyong National University)

Publication Information

Journal of Information Processing Systems / v.12, no.1, 2016 , pp. 100-108 More about this Journal

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;

Citations & Related Records

Reference

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. DOI
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/.