Research Of The Technology Of Online Handwriting Mongolia Words Recognition

In the 1980s, research of Mongolian characters input methods was begun. Most of input methods were concentrated on the keyboard code. But research of Mongolian characters recognition was quite little. Under the circumstances, we proposed to research and realize a recognition system for handwriting Mongolian characters, that can provide a new input method, which is quick, highly efficient and intelligent. The fundamental task of Online handwriting recognition is to take an input pattern, and the handwritten signals collected online via a digitizing device, and classify it as one of a pre-specified set of words (i.e., the system's lexicon). Because of large variation of handwriting, exact recognition is very difficult. Especially the connectivity between the characters, make the recognition more difficult.During recent years, the application of online handwriting recognition is more and more widespread, mainly due to the increasing popularity of the personal digital assistant (PDA). Now there are many products of online handwriting recognition of Chinese characters and English characters. Mongolia language is very popular among the Mongolia people in the North China, so the research of online handwriting Mongolia words recognition has a far-reaching meaning about developing the Minority information technology and national culture.This paper primarily discussed Online Handwriting Recognition methods for Mongolia words. We used in turn preprocessing technology based on removing the noise,letter segmentation method based on the structure of Mongolian language,the feature selection technology which include coarse classification features and fine classification features, as well as Multiple Classifier which combined HMM model and the DTW method and so on. Based on the above technology, we developed a Mongolian writing recognition experiment system. Experimental results show that writer-dependent words achieve recognition rates above 90%. And unconstrained words achieve recognition rates above 83%. Our system run well, and the recognition rate initially achieves the practical level.