Classification of newspaper image blocks using texture analysis

Abstract

An important step in the analysis of images of printed documents is the classification of segmented blocks into categories such as half-tone photographs, text with large letters, text with small letters, line drawings, etc. In this paper, a method to classify blocks segmented from newspaper images is described. It is assumed that homogeneous rectangular blocks are first segmented out of the image using methods such as run-length smoothing and recursive horizontal/vertical cuts. The classification approach is based on statistical textural features and feature space decision techniques. Two matrices, whose elements are frequency counts of black-white pair run lengths and black-white-black combination run lengths, are used to derive texture information. Three features are extracted from the matrices to determine a feature space in which block classification is accomplished using linear discriminant functions. Experimental results using different block segmentation results, different newspapers, and different image resolutions are given. Performance and speed with different image resolutions are indicated.