A density-based approach for text extraction in images

In this paper we describe a new approach to distinguish and extract text from images with various objects and complex backgrounds. The goal of our approach is to present characters in images with clear background and without other objects. The proposed approach mainly includes two steps. Firstly, a density-based clustering method is employed to segment candidate characters by integrating spatial connectivity and color feature of characterspsila pixels. In most images, colors of pixels in one character are commonly non-uniform due to the noise. So a new histogram segmentation method is proposed in this step to obtain the color thresholds of characters. Secondly, priori knowledge and texture-based method are performed on the candidate characters to filter the non-characters. Experimental results show that the proposed approach has a good performance in character extraction rate.