Fingertip-Writing Alphanumeric Character Recognition for Vision-Based Human Computer Interaction

This paper proposes a vision-based fingertip handwriting alphanumeric character recognition system to provide an alternative for human computer interaction. Traditional handwriting recognition systems are limited because they require a specific or expensive input device, such as pen, tablet, or touch panel. Recently, cameras have gradually become standard components in many computer-based products. Therefore, a fingertip and camera combination provides a flexible and convenient input device. This proposed system combines fingertip detection, trajectory feature extraction, and character recognition. First, fingertip moving trajectories are tracked and recoded. Then, the proposed cyclic chain code histograms are extracted from the trajectories as features. Finally, the proposed system adopts the sigmoid radial basis function neural networks with growing and pruning algorithm (SRBF-GAP) to recognize handwritten characters. Experimental results show that the proposed novel input system is feasible and effective. This study also presents possible applications for camera input devices.