Letter and Person Recognition in Freeform Air-Writing Using Machine Learning Algorithms

Air-writing is an emerging form of human-computer interaction that enables text entry through hand movements in the air. This paper explores air-writing-based person recognition alongside letter recognition to analyze the correlation between subjects and their writing styles. This analysis has potential applications in early childhood education for assessing cognitive and psychomotor skills, as well as in user authentication. A comprehensive framework for freeform air-writing analysis using a wearable glove is introduced, encompassing person and letter recognition, letter segmentation, feature extraction, and dataset generation. Unlike user authentication where person recognition depends on predefined content, such as a passcode or signature, person recognition here depends on the underlying writing style of the writer in free-form. The free-form air-writing indicates the user freedom to select any letter with any size (varying from a quarter to the full size of an A4 page) to write on the air. In the study, a dataset is also developed, containing the air-writing signals from the wearable glove, integrated with IMU sensors (gyroscope and accelerometer). Fourier and wavelet transforms are used to extract features and the performances of various machine learning algorithms, namely Decision Tree, Random-Forest, K-Nearest Neighbors, Support Vector Machine, Artificial Neural Networks, and SubSpace KNN, are comparatively studied. To the best of the authors’ knowledge, there is no study for person recognition from freeform air-writing letters through IMU sensors, and also there is no study using the Fourier and Wavelet features in this context. Furthermore, the study is also original due to its publicly available air-writing dataset on the Turkish alphabet and also applying various machine learning algorithms. The experimental results show that SubSpace KNN is superior to the others under the suggested parameter settings.