Arabic character recognition in air-writing based on motion data from 3-axis accelerometer

Abstract

Air-writing is a cutting-edge, non-touch human-machine interaction technique that enables users to input text into digital devices by moving their hands in free space, instead of using conventional input devices like keyboards and touch screens. This approach appears to be one of the most effective ways to enter text into digital systems in the future. English air-writing has received significant scholarly attention, but no studies on Arabic air-writing were found. In this research, a system is developed to recognize Arabic characters in air-writing based on motion data from a 3-axis accelerometer. A data acquisition system is constructed to record hand movements during air-writing. Each Arabic letter is written 25 times in the air using this data acquisition system, and a motion-sensor-based Arabic air-writing dataset is prepared. Using this dataset, several supervised machine learning models have been trained, and their accuracy has been determined. It is observed that the Fine KNN and Quadratic SVM models have demonstrated the highest accuracy (98.5%) in identifying Arabic characters from air-writing among the various available supervised machine learning models.