Handwritten Assamese numeral recognizer using HMM & SVM classifiers

Abstract

This work describes the development of Assamese online numeral recognition system using Hidden Markov Models (HMM) and Support Vector Machines (SVM). Preprocessed (x, y) coordinates and their first and second derivatives at each point are used as features for both the modeling techniques. The two systems are developed individually using HMM and SVM. The results from both the systems are then combined using two different approaches. In the first approach, the scores from both the classifiers are directly merged and an improvement in performance is observed in the combined system (Comb - 1). In the second approach, the confusion patterns from HMM and SVM classifiers are also analyzed. Based on this, the results are further combined to obtain a final hybrid numeral recognizer with an enhanced performance (Comb - 2). The HMM, SVM, Comb-1 and Comb-2 systems provide average recognition performance of 96.5, 96.8, 98 and 98.3, respectively.