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].
CITATION STYLE
Tang, Y. Y., Hou, Y., Song, J., & Yang, X. (2001). Mixture-state document segmentation using wavelet-domain hidden Markov tree models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2251, pp. 230–236). Springer Verlag. https://doi.org/10.1007/3-540-45333-4_29
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