联机手写维吾尔文字母识别方法

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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 38400 test samples of 128 classes, the proposed approach achieves an accuracy of 89.08%.

Key words： Online Handwritten Recognition Uyghur Characters Normalization Feature Extraction Classifier

Received: 28 February 2012

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	Articles by authors
	Mayire IBRAYIM
	ZHANG Heng
	LIU Cheng-Lin
	Askar HAMDULLA

Cite this article:

Mayire IBRAYIM,ZHANG Heng,LIU Cheng-Lin等. A Method for Online Handwritten Uyghur Character Recognition[J]. , 2012, 25(6): 979-986.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2012/V25/I6/979

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