A Method for Online Handwritten Uyghur Character Recognition
Mayire IBRAYIM1,3, ZHANG Heng2, LIU Cheng-Lin2, Askar HAMDULLA3
1.School of Electronic Information,Wuhan University,Wuhan 430072 2.The National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190 3.College of Information Science and Engineering,Xinjiang University,Urumqi 830046
Abstract An approach for online handwritten Uyghur character recognition is proposed based on the analysis of the unique shapes and writing styles of Uyghur characters. The various techniques of normalization, feature extraction and classification are evaluated that have been successfully applied in handwritten Chinese character recognition. Specifically, eight normalization techniques and the normalization cooperated feature extraction (NCFE) method with different settings are used. Four classifiers are used for classification including the modified quadratic discriminant function (MQDF), the discriminative learning quadratic discriminant function (DLQDF), the learning vector quantization (LVQ) classifier, and the support vector classifier with RBF kernel (SVC-rbf). Furthermore, the geometric features which characterize the spatial context in handwritten documents are extracted for enhancing the recognition performance. In experiments on 38400 test samples of 128 classes, the proposed approach achieves an accuracy of 89.08%.
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2012, Vol. 25 
