Integrating LCS and SVM for 3D handwriting recognition on handheld devices using accelerometers
Wang-Hsin Hsu, Yi-Yuan Chiang, Wen‐Yen Lin, Wei-Chen Tai, Jung-Shyr Wu · 2009
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 preprocess-ing: 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%.