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To achieve this, we carefully exploit acoustic pulse signals with high accuracy of time of fight (ToF) measurements. Then we employ trilateration localization method to capture the trajectory of handwriting in air. After that, we incorporate a data augmentation module to enhance the handwriting recognition performance. Finally, we customize a back propagation neural network that leverages augmented image dataset to train a model and recognize the acoustic system generated handwriting characters. We implement AcousticPAD prototype using cheap commodity acoustic sensors, and conduct extensive real environment experiments to evaluate its performance. 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