Context-based multiscale classification of document images using wavelet coefficient distributions

In this paper, an algorithm is developed for segmenting document images into four classes: background, photograph, text, and graph. Features used for classification are based on the distribution patterns of wavelet coefficients in high frequency bands. Two important attributes of the algorithm are its multiscale nature—it classifies an image at different resolutions adaptively, enabling accurate classification at class boundaries as well as fast classification overall—and its use of accumulated context information for improving classification accuracy.