MATEC Web Conf.
Volume 255, 2019Engineering Application of Artificial Intelligence Conference 2018 (EAAIC 2018)
|Number of page(s)||4|
|Section||Deep Learning and Big Data Analytic|
|Published online||16 January 2019|
Deep learning Convolutional Neural Network for Unconstrained License Plate Recognition
1 Department of Computer Science, Faculty of Engineering & Information Technology, Southern University College, Jalan Selatan Utama, Off Jalan Skudai, 81300 Skudai, Johor, Malaysia
2 Computer Centre, Southern University College, Jalan Selatan Utama, Off Jalan Skudai, 81300 Skudai, Johor, Malaysia
The evolve of neural networks algorithm into deep learning convolutional neural networks seems like the next generation for object detection. This algorithm works has a significantly better accuracy and did not tied to any particular aspect ratio. License plate and traffic signs detection and recognition have a number of different applications relevant for transportation systems, such as traffic monitoring, detection of stolen vehicles, driver navigation support or any statistical research. An exponential increase in number of vehicles necessitates the use of automated systems to maintain vehicle information. The information is highly required for both management of traffic as well as reduction of crime. Number plate recognition is an effective way for automatic vehicle identification. A number of methods have been proposed, but only for particular cases and working under constraints (e.g. known text direction or high resolution). Deep learning convolutional neural networks work well especially in handles occlusion/rotation better, therefore we believe this approach is able to provide a better solution to the unconstrained license plate recognition problem.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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