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In this article, we present a new method for automatic license plate recognition (ALPR) based on local power spectrum (LPS) features map and convolutional neural network (CNN). The multi-scaled and multi-oriented LPS features derived from log Gabor wavelets are well discussed. Hence, LPS at given orientation and scale is applied for license plate detection (LPD). Then, we apply an adaptive thresholding algorithm to LP character string for binarization. After that, characters are extracted separately to feed deep CNN for the Tunisian LPR. The proposed LPD approach is tested on Tunisian and Benchmark datasets under different conditions of complexities. Our developed system achieves about 96% accuracy on LPD and 95% on LPR.

A Vehicle License Plate Detection and Recognition Method Using Log Gabor Features and Convolutional Neural Networks