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

被引：1

作者：

Zaafouri, Ahmed ^{[1
]}

Sayadi, Mounir ^{[1
]}

Wu, Wei ^{[2
]}

机构：

[1] Univ Tunis, Lab Signal Image & Energy Mastery SIME, ENSIT, LR 13ES03, Tunis 1008, Tunisia

[2] Sichuan Univ, Sch Elect & Informat Engn, Chengdu, Peoples R China

来源：

CYBERNETICS AND SYSTEMS | 2023年 / 54卷 / 01期

关键词：

Automatic License plate recognition (ALPR); character recognition; convolutional neural network; Log Gabor wavelets; power spectrum features; LOCATION; LOCALIZATION; TRANSFORM; REGION;

D O I：

10.1080/01969722.2022.2055400

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：88 / 103

页数：16

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