Hand-written Numeral Recognition Based on Spectrum Clustering
In this paper, we First makes selection of the Zernike moment features of handwritten numerals based on the principles that the distinction degree of inside-class features is small and the dividing of the features between classes is huge; Then construct the similar matrix between handwritten numerals by the similarity measure based on Grey relational analysis and make transitivity transformation to similar matrix for better block symmetry after reformation; Finally make spectrum decomposition to the Laplacian matrix which from the reformation similar matrices, and recognize the handwritten numerals with the eigenvectors corresponding to the second minimal eigenvalues in Laplacian matrix as the spectral features. The experimental result indicates that the robustness of the algorithm proposed in this paper is great and the result is fine.
Zernike moment Spectrum Clustering hand-written numeral recognition
Shan Zeng Nong Sang Xiaojun Tong
Institute for pattern recognition and artificial intelligence, Huazhong University of Science and Te Institute for pattern recognition and artificial intelligence, Huazhong University of Science and Te Department of Mathematics and Physics, Wuhan Polytechnic University, Wuhan, Hubei 430023
国际会议
桂林
英文
1-8
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）