Paper Info
Title
A shape representation scheme for hand-drawn symbol recognition
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.
Year
DOI
Venue
2011
10.1007/978-3-642-25725-4_19
MIWAI
Keywords
Field
DocType
hand-drawn symbol recognition,lower order,rotation invariant,command gesture,zernike moment,shape representation scheme,higher order zernike moment,kullback-leibler distance,hand-drawn shape,authentication purpose,representation scheme,scale invariant,probability distribution,moments
Normalization (statistics),Invariant (physics),Pattern recognition,Symbol,Computer science,Zernike polynomials,Probability distribution,Artificial intelligence,Invariant (mathematics),Classifier (linguistics),Velocity Moments
Conference
Volume
ISSN
Citations 
7080
0302-9743
0
PageRank 
References 
Authors
0.34
11
3
Name
Order
Citations
PageRank
P. Viswanath114811.77
T. Gokaramaiah200.34
Gouripeddi V. Prabhakar Rao300.34