Paper Title:
A New Feature Fusion Method for Handwritten Character Recognition Based on 3D Accelerometer
  Abstract

In this paper, a new feature fusion method for Handwritten Character Recognition based on single tri-axis accelerometer has been proposed. The process can be explained as follows: firstly, the short-time energy (STE) features are extracted from accelerometer data. Secondly, the Frequency-domain feature namely Fast Fourier transform Coefficient (FFT) are also extracted. Finally, these two categories features are fused together and the principal component analysis (PCA) is employed to reduce the dimension of the fusion feature. Recognition of the gestures is performed with Multi-class Support Vector Machine. The average recognition results of ten Arabic numerals using the proposed fusion feature are 84.6%, which are better than only using STE or FFT feature. The performance of experimental results show that gesture-based interaction can be used as a novel human computer interaction for consumer electronics and mobile device.

  Info
Periodical
Edited by
Ran Chen
Pages
1583-1587
DOI
10.4028/www.scientific.net/AMM.44-47.1583
Citation
Z. Y. He, "A New Feature Fusion Method for Handwritten Character Recognition Based on 3D Accelerometer", Applied Mechanics and Materials, Vols. 44-47, pp. 1583-1587, 2011
Online since
December 2010
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