Similarity measure for CCITT group 4 compressed document images

Abstract

Similarity measure of document images acts a crucial role in the area of document image retrieval. A method of measuring the similarity of CCITT Group 4 compressed document images is proposed in this paper. The features are extracted directly from the changing elements of the compressed images. Weighted Hausdorff distance is utilized to assign all of the word objects from two document images to corresponding classes by an unsupervised classifier, whereas the possible stop words are excluded. Document vectors are built by the occurrence frequency of the word object classes, and the pair-wise similarity of two document images is represented by the scalar product of the document vectors. Five group articles relating to different domains are used to test the validity of the presented approach.