文章基本信息

标题：Multileveled ALPR using Block-Binary-Pixel-Sum Descriptor and Linear SVC
本地全文：下载
作者：B. Lavanya ; G. Lalitha
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：5
DOI：10.14569/IJACSA.2022.0130591
语种：English
出版社：Science and Information Society (SAI)
摘要：Automatic license plate recognition (ALPR) is es-sential component of security and surveillance. ALPR mainly aims to detect and prevent the crime and fraud activities; it also plays an important role in traffic monitoring. An algorithm is proposed for recognizing license plate candidates. The proposed work aimed to recognize the license plate of a car. Proposed work is designed in multilevel for more accurate License Plate (LP) recognition, At level 1 algorithm produced 93.5% accuracy and in level 3 algorithm gives 96% accuracy. For training and testing purpose, LP images were used from Medialab cars dataset, kaggle car dataset and goggle map images. These images in the dataset is formulated at various angles and illumination. Proposed algorithm for LP recognition is done by using the Block Binary Pixel descriptors (BBPS) and Linear Support Vector Classification (SVC). Proposed algorithm is novel and produces higher accuracy in minimal processing time of an average 0.42 milliseconds with 96% accuracy when compared with state-of-the art methods.
关键词：BBPS – Block Binary Pixel Sum; ALPR - Automatic License Plate Recognition; ROI - Region of Interest; SVC - Support Vector Classification; LP - License Plate