Spatio-Temporal Hough Forest for efficient detection–localisation–recognition of fingerwriting in egocentric camera

Recognising fingerwriting in mid-air is a useful input tool for wearable egocentric camera. In this paper we propose a novel framework to this purpose. Specifically, our method first detects a writing hand posture and locates the position of index fingertip in each frame. From the trajectory of the fingertip, the written character is localised and recognised simultaneously. To achieve this challenging task, we first present a contour-based view independent hand posture descriptor extracted with a novel signature function. The proposed descriptor serves both posture recognition and fingertip detection. As to recognising characters from trajectories, we propose Spatio-Temporal Hough Forest that takes sequential data as input and perform regression on both spatial and temporal domain. Therefore our method can perform character recognition and localisation simultaneously. To establish our contributions, a new handwriting-in-mid-air dataset with labels for postures, fingertips and character locations is proposed. We design and conduct experiments of posture estimation, fingertip detection, character recognition and localisation. In all experiments our method demonstrates superior accuracy and robustness compared to prior arts.