基于DWT和神经网络的车牌文本定位

Tianding Chen
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引用次数: 7

摘要

车牌文本自动定位是智能交通系统的重要研究课题之一。车牌文本提供了有关图像内容的高度浓缩的信息。提出了一种基于离散小波变换和神经网络的车牌文本定位方法。由于DWT系数提供了文本区域的重要信息,本文利用这些系数和几种图像处理技术对候选车牌文本区域进行初步定位。然后采用形态学扩张运算和神经网络相结合的方法提高车牌文本定位的准确率。研究的目的是提高车牌的识别率,提高车牌定位的成功率。实验结果表明,该方法能够成功地从复杂环境中定位文本区域。验证了该系统的可行性,实现了96%的板材定位正确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
License plate text localization using DWT and neural network
The license plate text automatic localization is one of the important research subjects in the intelligent transportation system. License plate texts provide highly condensed information about the contents of images. It proposes a license plate text localization method using discrete wavelet transform (DWT) and neural network. Because the DWT coefficients provide important information of text regions, this paper employs those coefficients and several image processing techniques to preliminarily locate candidate license plate text regions. Then the morphological dilation operation and the neural network are employed to raise the precision rate of the location for license plate text. The objective of the research is to increase the recognition rate of license plates and to improve the success rate of license plate locating. According to the experimental results, the proposed method can successfully locate text region from complex environment. It demonstrates the feasibility of this system and achieves 96% of the correct plate location rate.
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