An Efficient Method for License Plate Localization Using Multiple Statistical Features in a Multilayer Perceptron Neural Network

机译：多层感知器神经网络中使用多个统计特征进行车牌定位的有效方法

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摘要

Accurate license plate localization is the most important prerequisite in ANPR (Automatic Number Plate Recognition) systems. Majority of the existing algorithms use a single feature to obtain the license plate location which causes to potential false detections. In this article we propose a robust methodology using 16 statistical features while we still preserve real-time processing of the system which is a requirement for such applications. The proposed method uses a Vertical Projection technique and Discrete Fourier Transform (DFT) in order to extract multiple statistical features, as well as K-means clustering and multilayer perceptron neural network technique to identify the location of a license plate in an image. The method is compared with the state-of-the-art research in the field and the effectiveness of the method is evaluated for various types of license plates with different scripts.

机译：准确的车牌定位是ANPR（自动车牌识别）系统中最重要的先决条件。大多数现有算法使用单个功能来获取车牌位置，这可能导致错误的检测。在本文中，我们提出了使用16种统计功能的可靠方法，同时仍保留了系统的实时处理，这是此类应用程序所必需的。所提出的方法使用垂直投影技术和离散傅立叶变换（DFT）来提取多个统计特征，以及使用K均值聚类和多层感知器神经网络技术来识别图像中牌照的位置。将该方法与该领域的最新研究进行了比较，并针对具有不同脚本的各种类型的车牌评估了该方法的有效性。

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来源
《Conference on Artificial Intelligence and Robotics;Asia-Pacific International Symposium》|2018年|7-13|共7页
会议地点 Kish Island(IR)
作者
Mahdi Rezaei; Morteza Isehaghi;
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作者单位

Faculty of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran;

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关键词
Licenses; Discrete Fourier transforms; Feature extraction; Neural networks; Genetic algorithms; Training; Image color analysis;

机译：许可证；离散傅立叶变换；特征提取;神经网络;遗传算法；训练;图像色彩分析;

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4. An Efficient Method for License Plate Localization Using Multiple Statistical Features in a Multilayer Perceptron Neural Network [C] . Mahdi Rezaei, Morteza Isehaghi Conference on Artificial Intelligence and Robotics . 2018

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An Efficient Method for License Plate Localization Using Multiple Statistical Features in a Multilayer Perceptron Neural Network

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