Deepjoy Das, Rituparna Devi, SRM Prasanna, Subhankar Ghosh and Krishna Naik, IIT - Guwahati, India

This work emphasises on the development of Assamese online character recognition system using HMM and SVM and performs a recognition performance analysis for both models. Recognition models using HTK (HMM Toolkit) and LIBSVM (SVM Toolkit) are generated by training 181 different Assamese Stokes. Stroke and Akshara level testing are performed separately. In stroke level testing, the confusion patterns of the test strokes from HMM and SVM classifiers are compared. In Akshara level testing, a GUI (provided by CDAC-Pune) which is integrated with the binaries of HTK/LIBSVM and language rules (stores the set of valid strokes which makes a character) are used, manual testing is done with native writers to test the Akshara level performance for both models. Experimental results show that the SVM classifier outperforms the HMM classifier.

Support Vector Machines, Hidden Markov Models, Handwriting Recognition, Assamese, LIBSVM, HTK