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


Christian Gerber, Mokdong Chung, Journal of Information Processing Systems Vol. 12, No. 1, pp. 100-108, Mar. 2016  

https://doi.org/10.3745/JIPS.04.0022
Keywords: Convolutional Neural Network, Number Plate Detection, OCR
Fulltext:

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.


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Cite this article
[APA Style]
Gerber, C. & Chung, M. (2016). Number Plate Detection with a Multi-Convolutional Neural Network Approach with Optical Character Recognition for Mobile Devices. Journal of Information Processing Systems, 12(1), 100-108. DOI: 10.3745/JIPS.04.0022.

[IEEE Style]
C. Gerber and M. Chung, "Number Plate Detection with a Multi-Convolutional Neural Network Approach with Optical Character Recognition for Mobile Devices," Journal of Information Processing Systems, vol. 12, no. 1, pp. 100-108, 2016. DOI: 10.3745/JIPS.04.0022.

[ACM Style]
Christian Gerber and Mokdong Chung. 2016. Number Plate Detection with a Multi-Convolutional Neural Network Approach with Optical Character Recognition for Mobile Devices. Journal of Information Processing Systems, 12, 1, (2016), 100-108. DOI: 10.3745/JIPS.04.0022.