Comparison of Support Vector Machine and Neural Network in Character Level Discriminant Training for Online Word Recognition

Discrete Hidden Markov Model (HMM) and hybrid of Neural Network (NN) and HMM are popular methods in handwritten word recognition system. In the hybrid system, NN is used for character level recognition while HMM is used for producing word score based on the probability of the hypothesized characters combined. All reported results shows better recognition for the hybrid system due to better discrimination capability of the NN. A more recent recognition method based on Support Vector Machine (SVM) has been suggested as an alternative to NN. In speech recognition (SR), SVM has been successfully used in the context of a hybrid SVM/HMM system. It gives a better recognition result compared to the system based on hybrid NN/HMM. This paper describes part of the joint work between CAIRO, UTM and IRCCyN, Nantes, France in developing a hybrid SVM/HMM based word recognition system. It mainly compares NN and SVM in a word recognition system based on character level discriminant training.