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

Ying Liu, Fengling Zhuo, Jian Zhou*, Linjuan Kuang, Kaitao Tan, Haibao Lua, Jianbing Cai, Yihao Guo, Rongtao Cao, Yongqing Fu, Huigao Duan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
18 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)54276–54286
JournalACS Applied Materials and Interfaces
Issue number48
Early online date23 Nov 2022
Publication statusPublished - 7 Dec 2022


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