Number plates of vehicles operating in real world are sometimes difficult to recognize due to the number plate degradation (bent or dirty plates). To recognize the vehicle number from a number plate with severe degradation, good segmentation is necessary, which in turn requires good thresholding. This paper proposes a binarization method that combines the fast processing speed of global thresholding methods with the local thresholding methods' ability to adapt to lacal gray level characteristics. The proposed method overcomes the degradation of number plates quickly and maintains the widths of digit strokes uniform. The paper presents results of comparison with existing global and local thresholding methods.
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