TY - UNPB ID - cogprints8118 UR - http://cogprints.org/8118/ A1 - anshu, Mr. anshuman sharma TI - To Improve the Performance of Handwritten digit Recognition using Support Vector Machine Y1 - 2012/03/20/ N2 - 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 AV - public KW - Handwriting recognition KW - Support Vector Machine KW - Neural Network ER -