基于神经网络和模板匹配的四轮车辆车牌号码提取

Anurag Kumar, Dipti Verma
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引用次数: 1

摘要

车牌识别策略是智能交通框架和交通观测框架的重要研究课题之一。LPR框架具有许多逻辑用途,如停车费用的分摊、道路费用收费、交通信息分类、交通检查框架等。然而,LPR的建立是为了通过车辆标签的图片来分离车辆的数据。本文对LCR框架进行了详细的研究,并提出了一种将格式协调方法应用于字符图像识别度量的技术。这种新方法同样适用于印度的案例。它依赖于将这些车牌的图片存储在一个表格中,旁边是字符的概要,然后将这些部分与车牌单独协调。在不同的外部条件下对分离的标签图片进行了实验。结果识别精度为80%,该策略在0.306秒内完成车牌识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extraction of Numbers from the Number Plates of 4 Wheel Vehicles using Neural Network and Template Matching
One of the significant examination subjects of astute transportation framework and traffic observing framework is a License Plate Recognition (LPR) strategy. An LPR framework has a lot of logical utilization, for example, the installment of stopping expense, parkway cost charge, and traffic information assortment, traffic checking frameworks, etc. Nonetheless, LPR was set up to separate the data of vehicles by the picture of their tags. This paper presents a detail investigation of LCR framework and furthermore a proposed technique for applying the format coordinating methodology for character picture acknowledgment measure. The new methodology can be applied similarly to Indian cases. It depends on storing the picture of these number plates alongside a rundown of characters as passages in a table and afterward coordinating these sections individually with the vehicle plate. The new methodology is tried on different examples of separated tag pictures caught in outside condition. The outcome yield 80% acknowledgment precision, the strategy takes 0.306 seconds to play out the vehicle plate acknowledgment.
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