  Handwritten Numeral recognition plays a vital role in postal automation
services especially in countries like India where multiple languages and
scripts are used Discrete Hidden Markov Model (HMM) and hybrid of Neural
Network (NN) and HMM are popular methods in handwritten word recognition
system. The hybrid system gives better recognition result due to better
discrimination capability of the NN. A major problem in handwriting recognition
is the huge variability and distortions of patterns. Elastic models based on
local observations and dynamic programming such HMM are not efficient to absorb
this variability. But their vision is local. But they cannot face to length
variability and they are very sensitive to distortions. Then the SVM is used to
estimate global correlations and classify the pattern. Support Vector Machine
(SVM) is an alternative to NN. In Handwritten recognition, SVM gives a better
recognition result. The aim of this paper is to develop an approach which
improve the efficiency of handwritten recognition using artificial neural
network
