Multiscale document segmentation using wavelet-domain hidden Markov models

We introduce a new document image segmentation algorithm, HMTseg, based on wavelets and the hidden Markov tree (HMT) model. The HMT is a tree-structured probabilistic graph that captures the statistical properties of the coeecients of the wavelet transform. Since the HMT is particularly well suited to images containing singularities (edges and ridges), it provides a good classiier for distinguishing between diierent document textures. Utilizing the inherent tree structure of the wavelet HMT and its fast training and likelihood computation algorithms, we perform multiscale texture classiication at a range of diierent scales. We then fuse these multiscale classiications using a Bayesian probabilistic graph to obtain reliable nal segmentations. Since HMTseg works on the wavelet transform of the image, it can directly segment wavelet-compressed images, without the need for decompression into the space domain. We demonstrate HMTseg's performance with both synthetic and real imagery.