Context Based Multiscale Classi cation of Document Images Based on Wavelet Coe cient Distributions

This paper presents an algorithm for the segmentation of document images into four classes: background, photograph, text, and graph. Features used for classi cation are based on the distribution patterns of wavelet coe cients in high frequency bands. Two important attributes of the algorithm are its multiscale nature|it classi es an image at di erent resolutions adaptively, enabling accurate classi cation at class boundaries as well as fast classi cation overall| and its use of accumulated context information for improving classi cation accuracy. EDICS: IP 1.5 Segmentation Corresponding Author Robert M. Gray 261 Packard Building, EE Depart. Stanford, CA 94305 Phone: (650)723-4001, (650)723-6685 Fax: (650)723-8473 Email: [email protected] Jia Li is currently with Xerox Palo Alto Research Center, Palo Alto, CA 94304. Robert M. Gray is with the Information Systems Laboratory, Department of Electrical engineering, Stanford University, CA 94305, U.S.A. Email: [email protected], [email protected]. This work was supported by the National Science Foundation under NSF Grant No. MIP-931190 and by a gift from Hewlett-Packard, Inc. 1