Interpretation of Formal Semantics from Hand Gesture to Text using Proficient Contour Tracing Technique
Sankara Gomathi S1, Amutha S2, Sridhar G3, Jayaprakasan M4
1Sankara Gomathi S, Professor & Dean, Department of ECE, Malla Reddy Engineering College and Management Sciences, Affiliated to JNTUH, (Telangana), India.
2Amutha S, Assistant Professor, Ramanujan Centre for higher Mathematics, Alagappa University, Karaikudi (Tamil Nadu), India.
3Sridhar G, Professor, Department of ECE, St.Martin’s Engineering College, (Telangana), India.
4Jayaprakasan M, Joint Director, Directorate General of Training, MSDE, Government of India, (New Delhi), India.
Manuscript received on 16 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 2624-2629 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B13180982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1318.0982S1119
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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: As an assorted nation with numerous religion tongues, India has attempted to embrace an official, institutionalized gesture based communication. Where as in Indo-Pakistani communication through signing, is viewed as the prevalent sort utilized in South Asia. Many who are deaf or hard of hearing rely on sign language, to communicate. However the estimation of sign language are very unsophisticated and definitions of what counts as proficiency that varies depends on many factors. There are many existing systems which use shape parameters like orientation, palm centroid ,data gloves with 5 accelerometer sensors , and optical markers which reflect infrared light to recognise hand gestures of sign language. Background subtraction techniques used in these systems are K-means clustering ,boundary counters, Eigen backgrounds using Eigen values and wireless technology and bluetooth for connecting software for transmitting recognised hand gesture signals. They are not cost effective but, the accuracy is not met to the need. Whereas, In our proposed system we concentrate mainly to convert hand gestures to text using contour tracing technique to recognise hand gestures using normal webcam. The semantics are classified by support vector machine with trained datasets. The recognised hand gestures are displayed as text. Our main objective is to resolve the problem of facing interviewer for vocally impaired individuals. This helps them to build their confidence and eradicate their inferiority complex compared to other methods.
Keywords: Contour Tracing, Hand Gesture, SVM, Feature Extraction, TOF, IoT.
Scope of the Article: Text Mining