Title :
Kernel Modified Quadratic Discriminant Function for Online Handwritten Chinese Characters Recognition
Author :
Yang, Duanduan ; Jin, Lianwen
Author_Institution :
South China Univ. of Technol., Guangzhou
Abstract :
The modified quadratic discriminant function has been used successfully in handwriting recognition, which can be seen as a dot-product method by eigen- decomposition of the covariance matrix. Therefore, it is possible to expand MQDF to high dimension space by kernel trick. This paper presents a new kernel- based method, Kernel modified quadratic discriminant function (KMQDF) for online Chinese Characters Recognition. Experimental results show that the performance of MQDF is improved by the kernel approach.
Keywords :
covariance matrices; handwritten character recognition; matrix decomposition; covariance matrix eigen-decomposition; dot-product method; kernel modified quadratic discriminant function; online handwritten Chinese characters recognition; Character recognition; Covariance matrix; Eigenvalues and eigenfunctions; Handwriting recognition; Kernel; Machine learning; Pattern analysis; Pattern recognition; Probability density function; Space technology;
Conference_Titel :
Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on
Conference_Location :
Parana
Print_ISBN :
978-0-7695-2822-9
DOI :
10.1109/ICDAR.2007.4378672
