Multiscale segmentation of document images using M-band wavelets

Abstract

In this work we propose an algorithm for segmentation of the text and non-text parts of document image using multiscale feature vectors. We assume that the text and non-text parts have different textural properties. M-band wavelets are used as the feature extractors and the features give measures of local energies at different scales and orientations around each pixel of the M × M bandpass channel outputs. The resulting multiscale feature vectors are classified by an unsupervised clustering algorithm to achieve the required segmentation, assuming no a priori information regarding the font size, scanning resolution, type layout etc. of the document.