Automatic Recognition of Basic Strokes Based on FMCW Radar System

It has been demonstrated the advantage of basic stroke recognition algorithm in the field of human-computer interaction (HCI). However, most traditional techniques heavily rely on the touch-contact operations to obtain character information, which limits the further application in non-contact scenario such as germ infection environment, high/low temperature environment or scene for blind human. This paper proposes a non-contact and automatic basic stroke recognition algorithm for handwritten Chinese characters based on frequency modulated continuous wave (FMCW) radar system. First, the radar system collects intermediate frequency (IF) signal of the eight basic strokes given as follows:

(horizontal stroke),

(dot stroke),

(lift stroke),

(left falling stroke),

(bend stroke),

(right falling stroke),

(vertical stroke) and

(hook stroke). Second, a range-time sequence (RTS) is obtained from IF signal by the window fast Fourier transform (window-FFT) algorithm, and an azimuth-time sequence (ATS) is obtained from IF signal by the frequency domain Capon (FD-Capon) algorithm. Then, the feature area-framing, binarization and open operation (FA-FBO) algorithm is proposed to enhance the features of the above two sequences. After that, a feature map set containing RTS feature map (RTSFM) and ATS feature map (ATSFM) is obtained. Finally, a novel convolutional neural network (CNN) model is customized to perform the strokes classification task with these feature maps as input. Experimental results demonstrate that the proposed scheme is able to effectively recognize the eight basic strokes and achieve an average classification accuracy of 99.25%.