Rough-Fuzzy Clustering and M-Band Wavelet Packet for Text-Graphics Segmentation

  • Pradipta Maji
  • Shaswati Roy
  • Malay K. Kundu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)

Abstract

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.

Keywords

Wavelet Packet Document Image Feature Extraction Technique Dyadic Wavelet Document Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pradipta Maji
    • 1
  • Shaswati Roy
    • 1
  • Malay K. Kundu
    • 1
  1. 1.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia

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