TY - JOUR
AU - 
AB -  This paper deals with static and dynamic hand gesture (digits) recognition. The method provides a threefold novel contribution: (1) segmentation algorithm gives better results on any skin colour and any size of hand on complex and non-uniform background; (2) key frame finding algorithm and (3) the recognition technique of signs (static digits, alphabets and dynamic digits). We separate out key frames from a sequence of static gestures, which include correct gestures from a video sequence. The recognition efficiency of key frame detection is 93% using the proposed algorithm. The segmentation efficiency is almost 95%. Features are extracted using the proposed feature extraction algorithm, and gestures are recognised. We propose a novel algorithm for static and dynamic gesture recognition. The proposed algorithm shows recognition efficiency of 94.8% for static gestures and 94% for dynamic gestures. Keywords: hand segmentation; key frame selection; feature extraction; static and dynamic hand gesture recognition. Reference to this paper should be made as follows: Rokade, R.S. and Doye, D.D. (2016) `Sign recognition using key frame selection', Int. J. Signal and Imaging Systems Engineering, Vol. 9, Nos. 4/5, pp.320­332. Biographical notes: Rajeshree S. Rokade is currently pursuing PhD at the S.G.G.S. Institute of Engineering and Technology 
TI - Sign recognition using key frame selection
JF - International Journal of Signal and Imaging Systems Engineering
DO - 10.1504/IJSISE.2016.078256
DA - 2016-01-01
UR - https://www.deepdyve.com/lp/inderscience-publishers/sign-recognition-using-key-frame-selection-VZ4Hum5Cow
SP - 320
EP - 332
VL - 9
IS - 4-5
DP - DeepDyve
ER -