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

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: weighted 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 96.85%.