License plate detection is a key part in vehicle license plate recognition system. In this paper, we present a hybrid method for license plate detection from natural scene images for the all-day traffic surveillance environment. The proposed method includes two stages: rough detection and accurate detection. Coarse detection stage based on color edge and morphology can help finding the region of interest quickly; Accurate detection stage based on HOG and SVM accurately detect the vehicle license plate. The effectiveness of the proposed method has been proven by the experimental results on a large database of images.
Texts on road signs contain important information which is quite useful for potential applications. We proposed a robust method for detecting road sign text from urban street scenes under different weather conditions. First, color Segmentation and morphological operations are employed to obtain candidate regions, and contours of candidate regions are mainly concern. Then, a linear support vector machine (SVM) classifier is followed for shape classification after shape features based on edge orientation histogram (EOH) of contours are extracted. Finally, binarization of road sign images is achieved by k-means clustering in the S channel, multi-scale rules and strokes merging are referenced to extract texts. Experiment results on a large amount of images demonstrate the effectiveness of the proposed method.
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