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Real time mobile based license plate recognition system with neural networks

In this paper, the implementation of localizing and recognizing license plate in real time environment with a neural network using a mobile device is described. The neural networks used in this research are Convolutional Neural Network (CNN) and Backpropagation Feed Forward Neural Network (BPFFNN)....

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Bibliographic Details
Published in:Journal of physics. Conference series 2020-03, Vol.1502 (1), p.12032
Main Authors: Liew, Connie, Kim On, Chin, Alfred, Rayner, Tse Guan, Tan, Anthony, Patricia
Format: Article
Language:English
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Summary:In this paper, the implementation of localizing and recognizing license plate in real time environment with a neural network using a mobile device is described. The neural networks used in this research are Convolutional Neural Network (CNN) and Backpropagation Feed Forward Neural Network (BPFFNN). Image processing algorithm for pre-processing, localization and segmentation is chosen based on its ability to cope with limited computational resource in mobile device. The proposed license plate localization steps include combination of Sobel edge detection method and morphological based method. Detected license plate image is segmented using connected component analysis (CCA) and bounding box method. Each cropped character is fed into CNN or BPFFNN model for character recognition process. The neural network model was pretrained using desktop computer and then later exported and implemented in Android mobile device. The experiment was conducted in a moving vehicle on selected driving routes. The results obtained showed that CNN performed better compared to BPFFNN in a real time environment.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1502/1/012032