ONLINE HANDWRITING MONGOLIA WORDS RECOGNITION COMBINED BOTH HMM AND NEAREST NEIGHBOR CLASSIFIERS

This paper primarily discussed Online Handwriting Recognition methods for Mongolia words which being often used among the Mongolia people in the North China. We introduced the multiple classifiers which were built on different feature sets. We make use of Online and Offline information for feature selection. And online feature applied to HMM Classifier, offline feature applied to Nearest Neighbor Classifier. Our classification combined both HMM model and Nearest Neighbor Classifier, so we called Multiple Classifier Experimental results show that writer-dependent words achieve recognition rates above 95%, and partially unconstrained words, under restriction of the number of strokes can achieve to 92%.