Abstract
<p class="Abstract">Sign language recognition has emerged in concert of the vital space of analysis in computer Vision. The problem long-faced by the researchers is that the instances of signs vary with each motion and look. Thus, during this paper a completely unique approach for recognizing varied alphabets of Kannada linguistic communication is projected wherever continuous video sequences of the signs are thought of. The system includes of three stages: Preprocessing stage, Feature Extraction and Classification. Preprocessing stage includes skin filtering, bar histogram matching. Eigen values and Eigen Vectors were thought of for feature extraction stage and at last Eigen value weighted Euclidean distance is employed to acknowledge the sign. It deals with vacant hands, so permitting the user to act with the system in natural manner. We have got thought of completely different alphabets within the video sequences and earned a hit rate of 95.25%.</p>
Highlights
A sign language could be a language within which communication between individuals square measure created by visually sending the sign patterns to specific that means
We tend to had tested our system with 20 videos and achieved an honest success in it
A fast, novel and robust system was proposed for recognition of different alphabets of Kannada Sign Language for video sequences
Summary
A sign language could be a language within which communication between individuals square measure created by visually sending the sign patterns to specific that means. [3], Kannada linguistic communication was recognized mistreatment Eigen price weighted geometrician distance based mostly classifier with associate accuracy rate of ninety seven It removed the problem faced by for gestures mistreatment each hands. Amit kumar and Ramesh Kagalkar [14] paper address the hand gesture recognition system can provide an opportunity for deaf persons to communicate with normal people without the need of an interpreter or intermediate. Amit kumar and Ramesh Kagalkar [15] this paper presents an Automatic translation system for gesture of manual alphabets in Marathi sign language. It deals with images of bare hands, which allows the user to interact with the system in a natural way. A large set of samples has been used to recognize 43 isolated words from the standard Marathi sign language
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