Persian Handwritten Digit Recognition using Support Vector Machines

Abstract

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 Keyword: Handwriting recognition, Support Vector Machine, Neural Network 1. Introduction Handwritten Recognition refers to the process of translating images of hand-written, typewritten, or printed digits into a format understood by user for the purpose of editing, indexing/searching, and a reduction in storage size. Handwritten recognition system is having its own importance and it is adoptable in various fields such as online handwriting recognition on computer tablets, recognize zip codes on mail for postal mail sorting, processing bank check amounts, numeric entries in forms filled up by hand and so on. There are two distinct handwriting recognition domains; online and offline, which are differentiated by the nature of their input signals. In offline system, static representation of a digitized document is used in applications such as cheque, form, mail or document processing. On the other hand, online handwriting recognition (OHR) systems rely on information acquired during the production of the handwriting. They require specific equipment that allows the capture of the trajectory of the writing tool. Mobile communication systems such as Personal Digital Assistant (PDA), electronic pad and smart-phone have online handwriting recognition interface integrated in them. Therefore, it is important to further improve on the recognition performances for these applications while trying to constrain space for parameter storage and improving processing speed. Figure 1 shows an online handwritten Word recognition system. Many current systems use Discrete Hidden Markov Model based recognizer or a hybrid of Neural Network (NN)