Mixture-State Document Segmentation Using Wavelet-Domain Hidden Markov Tree Models

  • Authors:

  • Yuan Yan Tang;Yuhua Hou;Jinping Song;Xiaoyi Yang

  • Affiliations:

  • -;-;-;-

  • Venue:
  • WAA '01 Proceedings of the Second International Conference on Wavelet Analysis and Its Applications
  • Year:
  • 2001

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Abstract

In this paper we introduce a mixture-state document segmentation method based on wavelet and the hidden Markov tree (HMT) models. First we propose a three-state HMT segmentation method that is similar to those in the reference [1]. Then through comparing the difference weights to the three-density Gaussian mixture distribution of different textures, we find that background, text and image can be well approximated respectively by one-state and two-state and three-state HMT models. Then we get a new segmentation method, mixture-state HMT segmentation. Experiments with scanned document images indicate that the new approach improves the segmentation accuracy over the raw segmentation in [1].