Doctor unpredicted prescription handwriting prediction using triboelectric smart recognition

Abstract

Background Handwriting digits and character recognition have become more important in the digitized world due to the practical applications and to pass information. Different recognition systems have been developed and proposed to be used in different fields where high classification efficiency is needed. Still, there has the issue of lowering the recognition rate. Aim The main objective of this study is to construct an energy-efficient IoT model for doctor unpredicted prescription handwriting prediction that allows the medical words for recognition system to ensure efficient medicine service with the help of a smart recognition system. Methods The integration of IoT for recognizing handwriting characters is more efficient and robust than other techniques. This approach of proposing and constructing an energy-efficient model for doctor unpredicted prescription handwriting prediction allows medical word recognition with the help of triboelectric smart recognition. Results The result yields the digital twin for finding the character, health, and usage monitoring systems for individual prescription with the energy-efficient IoT-related ROI per Optical character recognition, Sensitivity analysis, Accuracy, Sensitivity, and specificity. Interpretation The observational investigation predicts that this article discusses the proposed technique’s design and architecture, which proves its methodology’s effectiveness, architecture, and successful implementation of its technology.