Sallauddin Mohmmad,Ramesh Dadi,A.Harshavardhan,Syed Nawaz Pasha,Shabana,



Static gesture recognition,PCA,Euclidean Distance,MATLABsoftware,


Generally communication with people in our daily life is by speaking with voice but some communications can possible with body language,facial expressions and hand signs. Expect the voice also we can communicate with others. Apart from that hand gestures are playing very important role in communication. Here we developed a gesture identification system which interpretsthe American Sign Language .This system helps the people who are deficiency with deaf and dumb. This system lead them to understand communicate as like normal people.Lot of proposals is introduced on gestures specified with their languages like ASL, ISL, etc.Here we are introducing new static gestures using MATLAB on bases of existing systems. Our input captured from camera then system applies the preprocessing on captured image. The set of features are retrieved using PCA. Comparison of the features is done using Euclidean Distance with the help of training sets. Finally optimal gestures identify and produce the output inwards of text or voice.


I. Anushree Pillai, Spandan Sinha, Piyanka Das,OinamRobitaChanu,”Contrivance OfRecognised Hand Gestures Into Voice And TextOutput,” Proceedings of 35th IRF International Conference, pp.41-45,2017.

II. C. Motoche, M.E. Benalcázar, “Real-time hand gesture recognition based on electromyographic signals and artificial neural networks,” International Conference on Artificial Neural Networks, pp. 352-361, 2018.

III. S. Saha, A. Konar, and J. Roy, “Single Person Hand Gesture Recognition Using Support Vector Machine,” Computational Advancement in Communication Circuits and Systems, Springer, pp. 161-167, 2015.

IV. Praveen P., Rama B. (2018) A Novel Approach to Improve the Performance of Divisive Clustering-BST. In: Satapathy S., Bhateja V., Raju K., Janakiramaiah B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542. Springer, Singapore

V. Pappula, Praveen, and Rama B. Ramesh Javvaji. “Experimental Survey on Data Mining Techniques for Association rule mining.” International Journal of Advanced Research in Computer Science and Software Engineering (2014).

VI. M Sheshikala, D Rajeswara Rao, R Vijaya Prakash, “A Map-Reduce Framework for Finding Clusters of Colocation Patterns-A Summary of Results”,Advance Computing Conference (IACC), 2017 IEEE 7th International, Pages 129-131.

VII. Mohammed Ali Shaik, P. Praveen and R. Vijaya Prakash, “Novel Classification Scheme for Multi Agents”, Asian Journal of Computer Science and Technology, Vol.8 No. S3, June 2019, ISSN: 2249-0701, pp. 54-58.

VIII. D. Kothandaraman, M. Shesikala, K. SeenaNaik, Y. Chanti, B. Vijyakumar, “Design of an Optimized Multicast Routing Algorithm for Internet of Things”, International Journal of Recent Technology and Engineering (IJRTE), vol. 8, Issue 2,2019.

IX. S. Saha, A. Konar, and J. Roy, “Single Person Hand Gesture Recognition Using Support Vector Machine,” Computational Advancement in Communication Circuits and Systems, Springer, pp. 161-167, 2015.

X. Joshi, C. Monnier, M. Betke, and S. Sclaroff, “Comparing random forest approaches to segmenting and classifying gestures,” Image and Vision Computing, vol. 58, pp. 86-95, 2017.

XI. Zhang, Y.; Cao, C.; Cheng, J.; Lu, H. Egogesture: a new dataset and benchmark for egocentric hand gesturerecognition. IEEE Trans. Multimedia 2018, 20, 1038–1050.

XII. Coteallard, U.; Fall, C.L.; Drouin, A.; Campeaulecours, A.; Gosselin, C.; Glette, K.; Laviolette, F.; Gosselin, B.Deep learning for electromyographic hand gesture signal classification using transfer learning. IEEE Trans.Neural Syst. Rehabil. Eng. 2019, 27, 760–771.

XIII. Rekha, J. Bhattacharya and S. Majumder, Shape, Texture and Local Movement Hand Gesture Features for Indian Sign Language Recognition , IEEE 2011.

XIV. Y. Xu, Y. Dai, “Review of hand gesture recognition study and application. Contemp, ” Eng. Sci.10, pp:375–384,2017

XV. H. Mizuno, N. Tsujiuchi, T. Koizumi, “Forearm motion discrimination technique using real-time EMG signals,” 2011 Annual International Conference of the IEEE, Engineering in Medicine and Biology Society, EMBC, pp. 4435–4438,2011.

View Download