This paper introduces a cost-effective robotic handwriting system designed to replicate human-like handwriting with high precision. Combining a Raspberry Pi Pico microcontroller, 3D-printed components, and a machine learning-based handwriting generation model implemented via TensorFlow, the system converts user-supplied text into realistic stroke trajectories. By leveraging lightweight 3D-printed materials and efficient mechanical designs, the system achieves a total hardware cost of approximately $56, significantly undercutting commercial alternatives. Experimental evaluations demonstrate handwriting precision within ±0.3 millimeters and a writing speed of approximately 200 mm/min, positioning the system as a viable solution for educational, research, and assistive applications. This study seeks to lower the barriers to personalized handwriting technologies, making them accessible to a broader audience.
Cite
@article{arxiv.2501.06783,
title = {Cost-Effective Robotic Handwriting System with AI Integration},
author = {Tianyi Huang and Richard Xiong},
journal= {arXiv preprint arXiv:2501.06783},
year = {2025}
}
Comments
This is an updated version of a paper originally presented at the 2024 IEEE Long Island Systems, Applications and Technology Conference (LISAT)