Integrating LCS and SVM for 3D handwriting recognition on handheld devices using accelerometers

  • Authors:

  • Wang-Hsin Hsu;Yi-Yuan Chiang;Wen-Yen Lin;Wei-Chen Tai;Jung-Shyr Wu

  • Affiliations:

  • Dept. of CSIE, Vanung University, Chungli, Taoyuan, Taiwan and Dept. of EE, National Central University, Chungli, Taoyuan, Taiwan;Dept. of CSIE, Vanung University, Chungli, Taoyuan, Taiwan;Dept. of EE, Vanung University, Chungli, Taoyuan, Taiwan;Dept. of DP, Ku-Pao Home Economics & Commercial High School, Taipei, Taiwan;Dept. of EE, National Central University, Chungli, Taoyuan, Taiwan

  • Venue:
  • CIT'09 Proceedings of the 3rd International Conference on Communications and information technology
  • Year:
  • 2009

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Abstract

Based on accelerometer, we propose a 3D handwriting recognition system in this paper. The system is consists of 4 main parts: (1) data collection: a single tri-axis accelerometer is mounted on a handheld device to collect different handwriting data. A set of key patterns have to be written using the handheld device several times for consequential processing and training. (2) data preprocessing: time series are mapped into eight octant of three-dimensional Euclidean coordinate system. (3) data training: LCS and SVM are combined to perform the classification task. (4) pattern recognition: using the trained SVM model to carry out the prediction task. To evaluate the performance of our handwriting recognition model, we choose the experiment of recognizing a set of English words. The accuracy of classification could be achieved at about 93%.