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International conference, IEEE, pp 338–343Įlmezain M, Al-Hamadi A, Michaelis B (2008) Real-time capable system for hand gesture recognition using Hidden Markov models in stereo color image sequences. Zhang Q, Chen F, Liu X (2008) Hand gesture detection and segmentation based on difference background image with complex background. In: Proceedings of the international multiconference of engineers and computer scientists, Citeseer, pp 19–21 Springer, pp 404–417Ĭhakraborty P, Sarawgi P, Mehrotra A, Agarwal G, Pradhan R (2008) Hand gesture recognition: a comparative study. In: European conference on computer vision. IJCSNS Int J Comput Sci Netw Secur 6:216–222īay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. Shin J-H, Lee JS, Kil SK, Shen DF, Ryu JG, Lee EH, Min HK, Hong SH (2006) Hand region extraction and gesture recognition using entropy analysis. graphics, vision and image process, Cairo. 26th annual international conference of the IEEE, IEEE, pp 1513–1516īinh ND, Ejima T (2005) Hand gesture recognition using fuzzy neural network. In: Engineering in Medicine and Biology Society, 2004. Lee J, Lee Y, Lee E, Hong S (2004) Hand region extraction and gesture recognition from video stream with complex background through entropy analysis. Lockton R (2002) Hand gesture recognition using computer vision 4th year project report, pp 1–69 Starner T, Pentland A (1997) Real-time American sign language recognition from video using hidden Markov models. IEEE Trans Pattern Anal Mach Intell 20:1371–1375 Starner T, Weaver J, Pentland A (1998) Real-time American sign language recognition using desk and wearable computer based video. Overall, it is hoped that the study may provide readers with a comprehensive introduction into the field of automated gesture and sign language recognition, and further facilitate future research efforts in this area. Further, we also discuss the challenges and limitations faced by gesture recognition research in general, as well as those exclusive to sign language recognition. The techniques reviewed are suitably categorized into different stages: data acquisition, pre-processing, segmentation, feature extraction and classification, where the various algorithms at each stage are elaborated and their merits compared. This paper provides a thorough review of state-of-the-art techniques used in recent hand gesture and sign language recognition research. One specific field of interest is sign language recognition. The ability of machines to understand human activities and their meaning can be utilized in a vast array of applications. Hand gesture recognition serves as a key for overcoming many difficulties and providing convenience for human life.

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