Learning to Recognize Handwriting Input with Acoustic Features

Abstract

For mobile or wearable devices with a small touchscreen, handwriting input (instead of typing on the touchscreen) is highly desirable for efficient human-computer interaction. Previous passive acoustic-based handwriting solutions mainly focus on print-style capital input, which is inconsistent with people's daily habits and thus causes inconvenience. In this paper, we propose WritingRecorder, a novel universal text entry system that enables free-style lowercase handwriting recognition. WritingRecorder leverages the built-in microphone of the smartphones to record the handwritten sound, and then designs an adaptive segmentation method to detect letter fragments in real-time from the recorded sound. Then we design a neural network named Inception-LSTM to extract the hidden and unique acoustic pattern associated with the writing trajectory of each letter and thus classify each letter. Moreover, we adopt a word selection method based on language model, so as to recognize legislate words from all possible letter combinations. We implement WritingRecorder as an APP on mobile phones and conduct the extensive experimental evaluation. The results demonstrate that WritingRecorder works in real-time and can achieve 93.2% accuracy even for new users without collecting and training on their handwriting samples, under a series of practical scenarios.