research-article

Learning to Recognize Handwriting Input with Acoustic Features

Authors:

Huadong MaAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 2

Article No.: 64, Pages 1 - 26

https://doi.org/10.1145/3397334

Published: 15 June 2020 Publication History

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.

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Cited By

Zeng QLi FZhao ZLi YWang Y(2024)AcouWrite: Acoustic-Based Handwriting Recognition on SmartphonesIEEE Transactions on Mobile Computing10.1109/TMC.2024.335148423:8(8557-8568)Online publication date: Aug-2024
https://doi.org/10.1109/TMC.2024.3351484
Chen WZheng CHe WZou YWu K(2024)Ultra Write: A Lightweight Continuous Gesture Input System with Ultrasonic Signals on COTS Devices2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494485(174-183)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerCom59722.2024.10494485
Chen QCui ZZhou ZTian YCao Z(2024)MMHTSR: In-Air Handwriting Trajectory Sensing and Reconstruction Based on mmWave RadarIEEE Internet of Things Journal10.1109/JIOT.2023.332525811:6(10069-10083)Online publication date: 15-Mar-2024
https://doi.org/10.1109/JIOT.2023.3325258
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Index Terms

Learning to Recognize Handwriting Input with Acoustic Features
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction techniques
      1. Text input

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 4, Issue 2

June 2020

771 pages

EISSN:2474-9567

DOI:10.1145/3406789

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Copyright © 2020 ACM.

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Published: 15 June 2020

Published in IMWUT Volume 4, Issue 2

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Cited By

Zeng QLi FZhao ZLi YWang Y(2024)AcouWrite: Acoustic-Based Handwriting Recognition on SmartphonesIEEE Transactions on Mobile Computing10.1109/TMC.2024.335148423:8(8557-8568)Online publication date: Aug-2024
https://doi.org/10.1109/TMC.2024.3351484
Chen WZheng CHe WZou YWu K(2024)Ultra Write: A Lightweight Continuous Gesture Input System with Ultrasonic Signals on COTS Devices2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494485(174-183)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerCom59722.2024.10494485
Chen QCui ZZhou ZTian YCao Z(2024)MMHTSR: In-Air Handwriting Trajectory Sensing and Reconstruction Based on mmWave RadarIEEE Internet of Things Journal10.1109/JIOT.2023.332525811:6(10069-10083)Online publication date: 15-Mar-2024
https://doi.org/10.1109/JIOT.2023.3325258
Yin YXie LJiang ZXiao FCao JLu S(2024)A Systematic Review of Human Activity Recognition Based on Mobile Devices: Overview, Progress and TrendsIEEE Communications Surveys & Tutorials10.1109/COMST.2024.335759126:2(890-929)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2024.3357591
Yin HKang LLi H(2024)Universal Handwriting Recognition for Mobile Devices via Acoustic SensingWireless Sensor Networks10.1007/978-981-97-1010-2_18(244-256)Online publication date: 1-Mar-2024
https://doi.org/10.1007/978-981-97-1010-2_18
Cheng HLou WYang YChen YZhang X(2023)TwinkleTwinkleProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962387:2(1-30)Online publication date: 12-Jun-2023
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Liberis ELane N(2023)Differentiable Neural Network Pruning to Enable Smart Applications on MicrocontrollersProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35694686:4(1-19)Online publication date: 11-Jan-2023
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Cheng HLou W(2023)PD-FMCW: Push the Limit of Device-Free Acoustic Sensing Using Phase Difference in FMCWIEEE Transactions on Mobile Computing10.1109/TMC.2022.316263122:8(4865-4880)Online publication date: 1-Aug-2023
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