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

• DOI: 10.1021/acsami.2c17943
• Publication Date: 2022-11-23T19:06:08Z
• Authors: Ying Liu; Fengling Zhuo; Jian Zhou; Linjuan Kuang; Kaitao Tan; Haibao Lu; Jianbing Cai; Yihao Guo; Rongtao Cao; YongQing Fu; Huigao Duan
• Keywords: Handwriting; Molecular Recognition

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