▎ 摘　　要

Machine-learning assisted handwriting recognition is crucial for development of next-generation biometric tech-nologies. However, most of the currently reported handwriting recognition systems are lacking in flexible sensing and machine learning capabilities, both of which are essential for implementa-tion 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 (similar to 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.