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/).
Cite this article
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 -