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A Shape Representation Scheme for Hand-Drawn Symbol Recognition

  • Pulabaigari Viswanath
  • T. Gokaramaiah
  • Gouripeddi V. Prabhakar Rao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7080)

Abstract

Pen based inputs are natural for human beings. A hand-drawn shape (symbol) can be used for various purposes, like, a command gesture, an input for authentication purpose, etc. Shape of a symbol is invariant to scale, translation, mirror-reflection and rotation of the symbol. Moments, like Zernike moments are often used to represent a symbol. Descriptors based on Zernike moments are rotation invariant, but since they are neither translation nor scale invariant, a normalization step as pre-processing is required. Apart from this, higher order Zernike moments are error prone. The present paper, proposes to use probability distributions of some local moments of lower order, as a representation scheme. Theoretically it is shown to possess all invariance properties. Experimentally, using the k-nearest neighbor classifier (with Kullback-Leibler distance), it is shown to perform better than Zernike moments based representation scheme.

Keywords

handwritten symbol recognition moments moment invariants probability distribution nearest neighbor classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pulabaigari Viswanath
    • 1
  • T. Gokaramaiah
    • 1
  • Gouripeddi V. Prabhakar Rao
    • 1
  1. 1.Departments of CSE and ITRajeev Gandhi Memorial College of Engineering & TechnologyNandyalIndia

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