A system with hidden markov models and gaussian mixture models for 3D handwriting recognition on handheld devices using accelerometers

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: hidden Markov models (HMMs) and Gaussian mixture models (GMMs) are combined to perform the classification task. (4) Pattern recognition: using the trained HMM 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 96.5%.