On-line Malayalam handwritten character recognition using HMM and SVM

Nowadays HMMs are widely used for the online recognition of handwriting characters in different languages, and they are dominating as one of the standard choices for time series classification among research communities. Disadvantages of the systems using HMM are large training time as well as higher computational cost especially at higher number of states. Support Vector Machines is another statistical method based on the structural risk minimization principle and is capable of dealing with features of higher dimensionality. This paper presents the comparative performance of On-line Malayalam handwritten character recognition system using HMM and SVM. Time domain features such as input co-ordinates and the angular features are basically used to form the feature vector. A novel feature extraction method based on discrete wavelet transform is used for the system using SVM. The system gives maximum accuracy of 97.97% for SVM and 95.24% for HMM when tested on 1279 character samples, also the system using SVM require very much less training time compared to those using HMM.