Hand-written numeral recognition based on spectrum clustering

Shan Zeng; Nong Sang; Xiaojun Tong

doi:10.1117/12.902047

2 December 2011 Hand-written numeral recognition based on spectrum clustering

Shan Zeng, Nong Sang, Xiaojun Tong

Proceedings Volume 8004, MIPPR 2011: Pattern Recognition and Computer Vision; 80040X (2011) https://doi.org/10.1117/12.902047
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

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.

Citation Download Citation

Shan Zeng, Nong Sang, and Xiaojun Tong "Hand-written numeral recognition based on spectrum clustering", Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 80040X (2 December 2011); https://doi.org/10.1117/12.902047

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