A New Classifier For Handwritten Chinese Character Recognition Using 2-Dimensional Functional Relationship Model
This paper presents a new classification method for online handwritten Chinese character recognition (HCCR). For classification, a similarity measure is established via statistical technique which calculates the coefficient of determination (R2p) for 2-dimensional unreplicated linear functional relationship (MULFR) model between the trajectory pattern of input character and character in database, according to which the recognition result is determined. The principle of the proposed method makes R2p, very robust against size and position variation as well as stroke shape deformation, without normalization. The efficiency of our proposed method is studied by the experimental result, showing that the proposed method still remains a promising recognition rate even without undergoing normalization if compared to city block distance with deviation (CBDD) and minimum distance (MD) classifier: a high recognition rate of 94％ with reduced processing time up to 77.85％.
coefficient of determination handwritten Chinese character recognition multidimensional functional relationship model statistical classifier
Y.F.Chang J.C.Lee W.L.Tong F.S.Gan
Department of Mathematical Sciences University of Tunku Abdul Rahman 46200 Petaling Jaya,Malaysia
国际会议
上海
英文
2530-2533
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）