NDLI: A new set of moment invariants for handwritten numeral recognition

Content Provider	IEEE Xplore Digital Library
Author	Feng Pan Keane, M.
Copyright Year	1994
Description	Author affiliation: Dept. of Exp. Phys., Univ. Coll. Galway, Ireland (Feng Pan; Keane, M.)
Abstract	In this paper, a new set of aspect invariant moments for handwritten numeral recognition are presented. These new moments exhibit two useful properties. Firstly, they are aspect invariant. This eliminates the need for size normalization of the unconstrained numerals. Secondly, their dynamic range remains constant with moment order. This overcomes the problem of diminishing high order moments, which occurs when other moment invariants are used. Thus, aspect invariant moments are particularly suitable for use with neural networks. Experimental results (using a multilayer perceptron and the backpropagation learning rule) show that a very high recognition rate (98.73%) and low substitution rate (1.06%) can be achieved on a totally unconstrained handwritten numeral database.
Starting Page	154
Ending Page	158
File Size	400770
Page Count	5
File Format	PDF
ISBN	0818669527
DOI	10.1109/ICIP.1994.413294
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-11-13
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Handwriting recognition Artificial neural networks Dynamic range Neural networks Shape Multilayer perceptrons Pattern recognition Physics Educational institutions Image databases
Content Type	Text
Resource Type	Article

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