AcouWrite: Acoustic-Based Handwriting Recognition on Smartphones

This research explores AcouWrite, a novel mobile handwriting recognition system leveraging acoustics and active acoustic sensing technologies. While conventional handwriting recognition systemstraditionally rely on visual orinertial sensors, these approaches often prove unsuitable in scenarios demanding privacy or hands-free interaction. AcouWrite innovatively employs short-time differential Channel Impulse Response (st-dCIR) combined with a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model to enable real-time, off-screen handwriting recognition. This paper provides an exhaustive account of AcouWrite's architecture, comprehensively compares it to signal- and gesture-based systems, and rigorously analyzes its accuracy, adaptability, and robustness across various devices. The conclusion presents an overview of existing challenges and potential future advancements within the burgeoning field of acoustic-based human-computer interaction.