Machine-Learning Assisted Handwriting Recognition Using Graphene Oxide-Based Hydrogel.

Journal: ACS applied materials & interfaces
Published Date: Nov 23, 2022

Abstract

Machine-learning assisted handwriting recognition is crucial for development of next-generation biometric technologies. However, most of the currently reported handwriting recognition systems are lacking in flexible sensing and machine learning capabilities, both of which are essential for implementation 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 for confidential information. This flexible and intelligent handwriting recognition system combines a printed circuit board with graphene oxide-based hydrogel sensors. It offers fast response and good sensitivity and allows high-precision recognition of handwritten content from a single letter to words and signatures. By analyzing 690 acquired handwritten signatures obtained from seven participants, we successfully demonstrate a fast recognition time (less than 1 s) and a high recognition rate (∼91.30%). Our developed handwriting recognition system has great potential in advanced human-machine interactions, wearable communication devices, soft robotics manipulators, and augmented virtual reality.

Authors

  • Ying Liu
    The First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
  • Fengling Zhuo
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha410082, China.
  • Jian Zhou
    CTIQ, Canon Medical Research USA, Inc., Vernon Hills, 60061, USA.
  • Linjuan Kuang
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha410082, China.
  • Kaitao Tan
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha410082, China.
  • Haibao Lu
    National Key Laboratory of Science and Technology on Advanced Composites in Special Environments, Harbin Institute of Technology, Harbin150080, China.
  • Jianbing Cai
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha410082, China.
  • Yihao Guo
    MR Collaboration, Siemens Healthcare Ltd, Guangzhou, China.
  • Rongtao Cao
    College of Mechanical and Vehicle Engineering, Hunan University, Changsha410082, China.
  • Yongqing Fu
    The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China.
  • Huigao Duan
    Engineering Research Center of Automotive Electrics and Control Technology, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China. Electronic address: duanhg@hnu.edu.cn.

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