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). 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.... [Show full abstract]
KeywordsConvolutional Neural Network; Backpropagation Feed Forward Neural Network; license plates; character recognition; Automatic License Plate Recognition
Fields of Research0801 Artificial Intelligence and Image Processing; 080108 Neural, Evolutionary and Fuzzy Computation; 170205 Neurocognitive Patterns and Neural Networks; 0906 Electrical and Electronic Engineering; 0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics; 0204 Condensed Matter Physics; 0299 Other Physical Sciences
TypeConference Contribution - Published (Conference Paper)
© The authors 2019. Published under licence in Journal of Physics: Conference Series by IOP Publishing Ltd.