AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Pen-input On-line Signature Verification with Position Pressure Inclination Trajectories
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recent results of online Japanese handwriting recognition and its applications
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
International Journal of Applied Mathematics and Computer Science
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A fast HMM algorithm is proposed for on-line hand written character recognition. After preprocessing input stroke are discretized so that a discrete HMM is used. This particular discretization naturally leads to a simple procedure for assigning initial state and state transition probabilities. In the training phase, complete marginelization with respect to state is not performed(Constrained Viterbi). A simple smoothing/flooring procedure yields fast and robust learning. A criterion based on normalized maximum likelihood ratio is given for deciding when to create a new model for the same character in the learning phase, in order to cope with stroke order variations and large shape variations. Preliminary experiments are done on the new Kuchibue data base from Tokyo University of Agriculture and Technology. The results seem encouraging.