Document Segmentation Using Polynomial Spline Wavelets

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 an effective tool for texture analysis. Based on the observation that text textures provide fast-changed and relatively regular distributed edges in the wavelet transform domain, an efficient document segmentation algorithm is designed via cubic B-spline wavelets. Three-means or two-means classification is applied for classifying pixels with similar characteristics after feature estimation at the outputs of high frequency bands of spline wavelet transforms. We examine and evaluate the contributions of different factors to the segmentation results from the viewpoints of decomposition levels, frequency bands and wavelet functions. Further performance analysis reveals the advantages of the proposed method