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.
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Viswanath, P., Gokaramaiah, T., Prabhakar Rao, G.V. (2011). A Shape Representation Scheme for Hand-Drawn Symbol Recognition. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2011. Lecture Notes in Computer Science(), vol 7080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25725-4_19
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DOI: https://doi.org/10.1007/978-3-642-25725-4_19
Publisher Name: Springer, Berlin, Heidelberg
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