| With the common use of various portable devices attached with magnetic pen, which can deliver more compact and comfortable input methods, online handwritten recognition technology is becoming a hot research topic in pattern recognition field. Online handwritten recognition technology can afford natural, easy human-computer interaction method for user. In the online handwritten recognition, track information is captured and machine recognizes instantaneously while the user writes using some special writing device such as magnetic pen on some writing tablet. The user can easily detect and correct misrecognized character. The advantage of online recognition is that the dynamic information of the pen movement can be captured in contrast to offline recognition.Now there are many products of online handwriting recognition for Chinese characters and Latin characters. However, the handwriting Uyghur character recognition is still in preliminary research stage. This paper carried out theoretical and experimental researches on the online handwritten character recognition, such as sampling of Uyghur characters, preprocessing, feature extraction and classifier design. In the sampling, the customized file format is designed to save data sample. In the pre-processing, to keep the structure information use smoothing and linear normalization, then resampling to improve calculation speed in next step. Use the gradient directional feature method for feature extraction, which combined with the structural features and statistical features. Classifier use support vector machine. Tests show, with the increasing of training data, the recognition rate reaches 90.62%,92.86%,94.53%,96.09%, respectively. Experimental results show, through gradient direction feature can get better results, up to 96.09%, the worst also higher than 90%. These achievements are also valuable for other similar characters, which have being applied in Xinjiang Uyghur Autonomous Region, such as Kazakh and Kyrgyz. |
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