摘要
为解决智能交通系统中道路标识牌字符提取问题,提出了一种快速的基于颜色与笔画的新算法。首先采用主元分析(PCA)方法提取标识牌颜色特征并进行定位;然后对确认后的标识牌区域进行仿射处理,获得容易进行文字提取的图像;最后根据形态学的top hat、skeleton算子以及区域生长等算法得出道路标识牌字符清晰的二值化图像,送光学符号识别(OCR)软件识别。实验结果显示该算法具有很强的准确性和鲁棒性。
A fast and robust approach for the text extraction of traffic signs based on color and stroke was proposed. First, a new color model derived from Karhunen-Loeve (KL) transform was applied to find all possible traffic sign candidates. Then, affine transformation was performed to restore traffic signs to let every road sign seem to be vertical to the camera optical axis which can improve the accuracy in detecting texts embedded in traffic signs. Finally, mathematical morphology and region growing algorithms were used to obtain a clearer binary picture which was sent to Optical Character Recognition (OCR) software. The experimental results demonstrate great robustness and efficiency of the proposed algorithm.
出处
《计算机应用》
CSCD
北大核心
2011年第1期266-269,共4页
journal of Computer Applications
基金
山东省中青年科学基金资助项目(2005BS011001)
关键词
主元分析
仿射变换
笔画算子
区域生长
Principal Component Analysis (PCA)
affine transformation
stroke-based algorithm
region growing