基于边缘直方图分析和分类器集成的车牌识别

M. Nejati, A. Majidi, Morteza Jalalat
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引用次数: 16

摘要

本文提出了一种新的伊朗车辆车牌识别方法。该算法包括车牌定位、分割、识别和多识别结果融合四个主要步骤。车牌定位首先进行降采样、去噪和直方图均衡化预处理。然后,使用垂直边缘直方图检测期望包含车牌的候选线。在这一步中,我们设计了一个过滤器,以减少假候选线的数量。然后利用边缘垂直投影直方图和纵横比特征提取候选板。通过局部自适应阈值分割得到候选对象的二值图像,并将其传输到分割和识别模块。我们的识别方法是使用混合专家架构的分类器集成来完成的。利用候选车牌识别结果的反馈,检测出真实的候选车牌。为了提高识别精度和鲁棒性,我们将所提出的LPR应用于不同曝光时间拍摄的车辆连续三帧图像,然后将它们的识别输出进行组合。白天和夜间实际情况下的实验结果表明,该方法的车牌定位精度为95.39%,识别后的整体精度为92.45%。
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License plate recognition based on edge histogram analysis and classifier ensemble
In this paper, a new approach for Iranian vehicle license plate recognition (LPR) is proposed. The proposed algorithm contains four main steps including plate localization, segmentation, recognition, and fusion of multiple recognition results. The license plate localization is begun with some preprocessing for down-sampling, denoising and histogram equalization. Then, histogram of vertical edges is used for detection of candidate lines expected to contain the license plate. In this step, we design a filter in order to reduce the number of false line candidates. The candidate plates are then extracted using vertical projection histogram of edges and aspect ratio characteristic. The binary image of these candidates obtained by locally adaptive thresholding is transmitted to the segmentation and recognition modules. Our recognition method is accomplished using a classifier ensemble with mixture of experts architecture. Using a feedback from the recognition result of candidate plates, the true candidate is detected. To improve the recognition accuracy and robustness, we apply the proposed LPR on three consecutive frames of a vehicle captured by different exposure times and then combine their recognition outputs. The experimental results in practical situations of day and night show that the proposed approach leads to 95.39% accuracy in plate localization and 92.45% overall accuracy after recognition.
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