Analysis of Handwriting Identification Based on Spectral Clustering

DOI

10.2991/meici-16.2016.168How to use a DOI?

Keywords

Spectral clustering; Handwriting identification; Data mining; Module identification

Abstract

This paper discusses the analysis of handwriting based on spectral clustering. It is presented that almost figures and letters can be identified from the approach of spectral clustering. The key idea of our approach is that a novel spectral clustering via local projection distance measure is proposed. With the requisite quantity of figure identification has pay more attention from other areas. According to the existing data were described a similarity affinity matrix or Laplacian matrix, in which computed the eigenvalue and eigenvector of the upon matrix and choose the suitable characteristic vector clustering of different data points.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jian Zhou
AU  - Ning Cai
AU  - Xiaokun Liu
PY  - 2016/09
DA  - 2016/09
TI  - Analysis of Handwriting Identification Based on Spectral Clustering
BT  - Proceedings of the 2016 6th International Conference on Management, Education, Information and Control (MEICI 2016)
PB  - Atlantis Press
SP  - 807
EP  - 810
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-16.2016.168
DO  - 10.2991/meici-16.2016.168
ID  - Zhou2016/09
ER  -