Wavelet transforms have been widely used as effective tools in texture segmentation in the past decade. Segmentation of document images, which usually contain three types of texture information: text, picture and background, can be regarded as a special case of texture segmentation. B-spline wavelets possess some desirable properties such as being well localized in time and frequency, and being compactly supported, which make them a good approach to texture analysis. In this paper, cubic B-spline wavelets are applied to document images; thereafter, each texture is featured by several regional and statistical characteristics estimated at the outputs of high frequency bands of spline wavelet transforms. Then three-means classification is applied for classifying pixels which have similar features. We also examine and evaluate the contributions of different factors to the segmentation results from the viewpoints of decomposition levels, frequency bands and feature selection, respectively.