Document image segmentation using wavelet scale-space features

An efficient and computationally fast method for segmenting text and graphics part of document images based on textural cues is presented. We assume that the graphics part have different textural properties than the nongraphics (text) part. The segmentation method uses the notion of multiscale wavelet analysis and statistical pattern recognition. We have used M-band wavelets which decompose an image into M×M bandpass channels. Various combinations of these channels represent the image at different scales and orientations in the frequency plane. The objective is to transform the edges between textures into detectable discontinuities and create the feature maps which give a measure of the local energy around each pixel at different scales. From these feature maps, a scale-space signature is derived, which is the vector of features at different scales taken at each single pixel in an image. We achieve segmentation by simple analysis of the scale-space signature with traditional k- means clustering. We do not assume any a priori information regarding the font size, scanning resolution, type of layout, etc. of the document in our segmentation scheme.