Method of Text Image Binarization Processing Using Histogram and Spectral Clustering
基于灰度直方图和谱聚类的文本图像二值化方法

The located text regions need to be binarized for accurate recognition in automatic textural extraction. Due to the complex backgrounds, traditional thresholding methods can not segment the character image effectively from natural scenes. A novel approach of binarization is proposed for gray images. The proposed algorithm uses the Normalized graph cut(Ncut) as the measure for spectral clustering, and the weighted matrices used in evaluating the graph cuts are based on the gray levels of an image, rather than the commonly used image pixels. Thus, the proposed algorithm requires much smaller spatial costs and much lower computation complexity. Experiments on text images in natural scene show the superior performance of the proposed method compared to the typical thresholding algorithms.