Moment Invariant Features used as Inputs to a NeuraI Network to Recognise Numerals

This paper presents an algorithm in which the moment invariant features of a numerical character are presented as inputs to a neural network. The image is digitised, by a frame grabber card plugged into the extension slot of an IBM AT computer. Three moment invariant features derived from the geometrical moments were extracted and fed to the neural network. The neural network, used back propagation learning in its training, classifies these features and attempts to produce the desired output. Noisefree and noisy images of various variances of the ten numerals were experimented and the results are reported.