3D handwriting recognition of smartphone based on LSTM
Traditional sensors are prone to receive the interference of external environmental factors due to achieving human-machine interaction in specific spatial area.3D handwriting recognition of smartphones based on the long short-term memory(LSTM)neural network is proposed,which can be used in human-machine interaction in non-specific 3D spaces.First,three-axis acceleration sensors of smartphones are used to collect data which perform pre-processing operations to construct a 3D handwriting recognition dataset.Then,the 3D handwriting recognition model based on LSTM is constructed and pre-trained by a-dopting the constructed datasets.Finally,the trained model is applied to implement 3D handwriting classi-fication recognition for smartphones.By testing on a self-built non-dependent user dataset,experimental results show that the proposed model can achieve the accuracy rate of 86.4％,recall rate of 88.1％,preci-sion rate of 88.4％,and F1 score of 88.0％.
万方数据
维普
