Rough-Fuzzy Clustering and M-Band Wavelet Packet for Text-Graphics Segmentation
This paper presents a segmentation method, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique, for documents having both text and graphics regions. It assumes that the text and non-text regions of a given document are considered to have different textural properties. The M-band wavelet packet is used to extract the scale-space features, which is able to zoom it onto narrow band high frequency components of a signal. A scale-space feature vector is thus derived, taken at different scales for each pixel in an image. Finally, the rough-fuzzy-possibilistic c-means algorithm is used to address the uncertainty problem of document segmentation. The performance of the proposed technique, along with a comparison with related approaches, is demonstrated on a set of real life document images.
KeywordsWavelet Packet Document Image Feature Extraction Technique Dyadic Wavelet Document Segmentation
- 1.Srihari, S.N.: Document Image Understanding. In: Proc. Fall Joint Computer Conference, pp. 87–96 (1986)Google Scholar
- 4.Lee, G.B., Odoyo, W.O., Lee, J.H., Chung, Y., Cho, B.J.: Two Texture Segmentation of Document Image Using Wavelet Packet Analysis. In: Proc. 9th Intl. Conf. Advanced Communication Technology, vol. 1, pp. 395–398 (2007)Google Scholar
- 8.Acharyya, M., Kundu, M.K.: Image Segmentation Using Wavelet Packet Frames and Neuro-Fuzzy Tools. Intl. Jrnl. Computational Cognition 5(4), 27–43 (2007)Google Scholar