Malayappan Shridhar

TL;DR: The authors show that the new method of deriving Zernike moment invariants along with the new normalization scheme yield the best overall performance even when the data are degraded by additive noise.

TL;DR: The authors describe the results of their investigation into the development of a recognition algorithm for identifying numerals that may be isolated or connected, broken or continuous using a structural classification scheme using a tree classifier.

TL;DR: A new two-stage character recognition algorithm yielding accuracies in excess of 98% and retaining computational simplicity is presented.

TL;DR: An efficient and fast algorithm for the direct computation of Zernike moments is presented, based on using some properties of ZERNike polynomials.

TL;DR: This paper presents a hierarchical neural network architecture for recognition of handwritten numeral characters that combines the decomposition of the global network into two independent neural networks facilitates rapid and efficient training of the individual neural networks.