基于特征训练神经网络的改进OCR车牌自动识别

Bhavin Kakani, Divyang Gandhi, S. Jani
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引用次数: 47

摘要

近年来,智能交通算法的重大研究和发展受到越来越多的关注。一个自动化、快速、准确和强大的车牌识别系统已成为交通管制和交通法规执法的需要;解决方案是ANPR。本文研究了一种改进的基于OCR的车牌识别技术,该技术采用神经网络训练的目标特征数据集。提出了一种混合车牌识别算法,并与现有方法进行了比较,提高了识别精度。整个系统可分为车牌定位、车牌字符分割和车牌字符识别三大模块。该系统在300张国内外机动车LP图像上进行了仿真,仿真结果满足了系统的主要要求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved OCR based automatic vehicle number plate recognition using features trained neural network
Significant research and development of algorithms in intelligent transportation has grabbed more attention in recent years. An automated, fast, accurate and robust vehicle plate recognition system has become need for traffic control and law enforcement of traffic regulations; and the solution is ANPR. This paper is dedicated on an improved technique of OCR based license plate recognition using neural network trained dataset of object features. A blended algorithm for recognition of license plate is proposed and is compared with existing methods for improve accuracy. The whole system can be categorized under three major modules, namely License Plate Localization, Plate Character Segmentation, and Plate Character Recognition. The system is simulated on 300 national and international motor vehicle LP images and results obtained justifies the main requirement.
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