Machine-learning assisted handwriting recognition using graphene oxide-based hydrogel

Liu, Ying, Zhuo, Fengling, Zhou, Jian, Kuang, Linjuan, Tan, Kaitao, Lua, Haibao, Cai, Jianbing, Guo, Yihao, Cao, Rongtao, Fu, Yong Qing and Duan, Huigao (2022) Machine-learning assisted handwriting recognition using graphene oxide-based hydrogel. ACS Applied Materials & Interfaces. ISSN 1944-8244 (In Press)

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Abstract

Machine-learning assisted handwriting recognition is crucial for development of next-generation biometric technologies. However, most of currently reported handwriting recognition systems are lacked in flexible sensing and machine learning capabilities, both of which are essential for implementations of intelligent systems. Herein, assisted by machine learning, we develop a new handwriting recognition system, which can be applied as both a recognizer for written texts and an encryptor i for confidential nformation. This flexible and intelligent handwriting recognition system combines a printed circuit board with graphene oxide and good sensitivity, and allows high-- based hydrogel sensors. It offers fast response precision recognitions of handwritten conten ts from a single letter to words and signatures. By analyzing 690 acquired handwritten signatures obtained from 7 participants, we successfully demonstrate a fast recognition time (less than 1 s) and a high recognition rate (~91.30). Our developed handwri has great potentials in advanced humanting recognition system machine interactions, wearable communication devices, soft robotics manipulators, and augmented virtual reality.

Item Type: Article
Additional Information: Funding information: This work was supported by the NSFC (No. 52075162), Hunan Excellent Yout (2021JJ20018), Hunan Science & Technology 2021GK4014), and International Exchange Grant (IEC/NSFC/201078) through Royal Society and the NSFCC.
Uncontrolled Keywords: Handwriting recognition, hydrogel, machine learning, stretchable sensor, human-machine interaction
Subjects: F200 Materials Science
G400 Computer Science
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: John Coen
Date Deposited: 15 Nov 2022 15:14
Last Modified: 15 Nov 2022 15:15
URI: https://nrl.northumbria.ac.uk/id/eprint/50659

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