Multiscale document segmentation using wavelet-domain hidden Markov models

Hyeokho Choi; Richard G. Baraniuk

doi:10.1117/12.373498

22 December 1999 Multiscale document segmentation using wavelet-domain hidden Markov models

Hyeokho Choi, Richard G. Baraniuk

Proceedings Volume 3967, Document Recognition and Retrieval VII; (1999) https://doi.org/10.1117/12.373498
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

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 coefficients of the wavelet transform. Since the HMT is particularly well suited to images containing singularities (edges and ridges), it provides a good classifier for distinguishing between different document textures. Utilizing the inherent tree structure of the wavelet HMT and its fast training and likelihood computation algorithms, we perform multiscale texture classification at a range of different scales. We then fuse these multiscale classifications using a Bayesian probabilistic graph to obtain reliable final 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.

Citation Download Citation

Hyeokho Choi and Richard G. Baraniuk "Multiscale document segmentation using wavelet-domain hidden Markov models", Proc. SPIE 3967, Document Recognition and Retrieval VII, (22 December 1999); https://doi.org/10.1117/12.373498

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