Abstract
This paper proposes a vision-based fingertip handwriting recognition system to provide an alternative to input devices. 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. The proposed system combines fingertip detection, trajectory feature extraction, and character recognition. First, fingertip moving trajectories are tracked and recoded. The proposed cyclic chain code histograms are then obtained from the trajectories and used as features in the following recognition process. An improved radial basis function (RBF) neural network is used 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.
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Acknowledgment
We would like to thank the National Science Council (Grant number: NSC 98-2221-E-155-050) for supporting this work.
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Lee, CC., Shih, CY., Yu, CC. et al. Vision-Based Fingertip-Writing Character Recognition. J Sign Process Syst 64, 291–303 (2011). https://doi.org/10.1007/s11265-010-0490-9
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DOI: https://doi.org/10.1007/s11265-010-0490-9