An Efficient Approach for Automatic Number Plate Recognition for Low Resolution Images

T. Kumar, Suraj Gupta, D. S. Kushwaha
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引用次数: 12

Abstract

Apart from the developed countries, many developing countries are facing the wrath of internal and external security. Inadequate resources force these countries to adopt the usage of Automatic Number Plate Recognition (ANPR) system to ramp up security to a certain extent. These countries use CCTV cameras for manual surveillance. The proposed ANPR system is designed to work with these CCTV cameras. Fundamental components of the image are for image enhancement, character segmentation and character recognition. Novel techniques for each of these components are the main contributions of this work. The proposed approach has been tested on both low and high resolution images. In low resolution images, the average accuracy in character recognition achieved by the proposed approach is 96.2% and the average accuracy in character segmentation is 100% whereas the other existing techniques are unable to recognize the number plates. In good quality images, the proposed approach achieves 100% accuracy in character segmentation and 95.3% average accuracy in character recognition. The approach achieves 4.5% improvement over other existing approaches in character segmentation for good quality images.
低分辨率车牌自动识别的一种有效方法
除了发达国家,许多发展中国家也面临着内外安全的愤怒。资源不足迫使这些国家采用自动车牌识别(ANPR)系统在一定程度上提高安全性。这些国家使用闭路电视摄像头进行人工监控。拟议的ANPR系统设计用于与这些闭路电视摄像机一起工作。图像的基本组成部分包括图像增强、字符分割和字符识别。这些组件的新技术是这项工作的主要贡献。该方法已在低分辨率和高分辨率图像上进行了测试。在低分辨率图像中,字符识别的平均准确率为96.2%,字符分割的平均准确率为100%,而现有的其他技术都无法识别车牌。在高质量图像中,字符分割准确率达到100%,字符识别准确率达到95.3%。该方法在对高质量图像进行字符分割时,比现有方法提高了4.5%。
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