Number detection in natural image with boosting classifier

Abstract

The number detection is useful in various applications such as license plate localization, detection of number button in elevator, and detection of exit number sign in public transport station. In this paper, we propose number detection methods in natural image using AdaBoost based on Modified Census Transform (MCT) features. It is a difficult task to detect numbers, characters, and specific symbols, because natural image includes many noises. Especially, illumination change is one of the most annoying sources of noise in the field of number detection based on image processing. Our number detection method uses many MCT features, which are robust to illumination change and AdaBoost for the feature selection to overcome this restriction. Experimental results show that the proposed method has a high detection rate in our license plate database which has been captured in the natural environment.