Sign Language Recognition System for Disabled People
Niroshini Infantia. C
Mrs. C. Niroshini Infantia, St. Joseph’s Institute of Technology, OMR, Chennai (Tamil Nadu), India.
Manuscript received on 22 November 2019 | Revised Manuscript received on 03 December 2019 | Manuscript Published on 14 December 2019 | PP: 48-52 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10121191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1012.1191S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The aim is to present a real time system for hand gesture recognition on the basis of detection of some meaningful shape based feature like orientation, center of mass, status of fingers in term of raised or folded fingers of hand and their respective location in image. Hand gesture Recognition System has various real time applications in natural, innovative, user friendly way of how to interact with the computer which has more facilities that are familiar to us. Gesture recognition has a wide area of application including Human machine interaction, sign language, game technology robotics etc are some of the areas where Gesture recognition can be applied. More specifically hand gesture is used as a signal or input means given to the computer especially by disabled person. Being an interesting part of the human and computer interaction hand gesture recognition is needed for real life application, but complex of structures presents in human hand has a lot of challenges for being tracked and extracted. Making use of computer vision algorithms and gesture recognition techniques will result in developing low-cost interface devices using hand gestures for interacting with objects in virtual environment. SVM (support vector machine) and efficient feature extraction technique is presented for hand gesture recognition. This method deals with the dynamic aspects of hand gesture recognition system.
Keywords: Human Gesture Recognition, Sign language, SVM.
Scope of the Article: Pattern Recognition