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Automatic Vehicle License Plate Recognition System Based on Image Processing and Template Matching Approach

机译:基于图像处理和模板匹配的汽车牌照自动识别系统

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A vehicle license plate recognition system is an important proficiency that could be used for identification of engine vehicle all over the earth. It is valuable in numerous applications such as entrance admission, security, parking control, road traffic control, and speed control. However, the system only manages to identify the license number and needs an operator to control the collected data. Therefore, this paper proposes an automatic license plate recognition system by using the image processing and template matching approach. The current study aims to increase the efficiency of license plate recognition system for Universiti Malaysia Perlis (UniMAP) smart university. This venture comprises of simulation program to recognize license plate characters where a captured image of vehicles will be the input. Then, these images will be processed using several image processing techniques and optical character recognition method in order to recognize the segmented number plate. The image processing techniques consist of colour conversion, image segmentation using Otsu's thresholding, noise removal, image subtraction, image cropping and bounding box feature. The optical character recognition based on template matching approach is used to analyse the printed characters on the segmented license plate image and to produce an output data consisting of characters. Overall, the proposed automatic vehicle license plate recognition system is capable to perform the recognition process by successfully recognizing license plate of 13 cars, from a total of 14 cars.
机译:车辆牌照识别系统是一种重要的能力,可用于在整个地球上识别发动机车辆。它在许多应用中都很有价值,例如入口准入,安全,停车控制,道路交通控制和速度控制。但是,该系统仅设法标识许可证号,并且需要操作员控制收集的数据。因此,本文提出了一种基于图像处理和模板匹配的自动车牌识别系统。当前的研究旨在提高马来西亚玻璃大学(UniMAP)智能大学的车牌识别系统的效率。该项目包括模拟程序,用于识别车牌字符,其中将捕获的车辆图像作为输入。然后,将使用几种图像处理技术和光学字符识别方法对这些图像进行处理,以识别分段的车牌。图像处理技术包括颜色转换,使用Otsu阈值的图像分割,噪声去除,图像减法,图像裁剪和边框功能。基于模板匹配的光学字符识别用于分析分割后的车牌图像上的印刷字符,并产生包含字符的输出数据。总体而言,建议的自动车牌识别系统能够通过成功识别总共14辆车中的13辆车的车牌来执行识别过程。

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