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Real-time hand gesture recognition - sign language to voice conversion



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The main aim of the project is to implement a real-time system that converts hand gestures from the American Sign Language(ASL) to voice signals. This could be used by the people with speaking disabilities to communicate with other people. For this purpose, surface Electromyogram(EMG) which is the measure of electrical activity from the forearm muscle and accelerometer which measures the acceleration of the hand motions were used. A wearable data collection device called Shimmer was used for recording these signals from the user and transmitting the collected data to Matlab using Bluetooth. A pattern recognition algorithm was implemented in Matlab which processed the signals and displayed output decisions based on the gesture that was performed. Several tests were performed on one-human subject to identify multiple single-handed and two-handed gestures to recognize various word as well as basic sentences in the ASL. The experimental results were recorded and discussed.






The main aim of the project is to implement a real-time system that converts hand gestures from the American Sign Language(ASL) to voice signals. This could be used by the people with speaking disabilities to communicate with other people. For this purpose, surface Electromyogram(EMG) which is the measure of electrical activity from the forearm muscle and accelerometer which measures the acceleration of the hand motions were used. A wearable data collection device called Shimmer was used for recording these signals from the user and transmitting the collected data to Matlab using Bluetooth. A pattern recognition algorithm was implemented in Matlab which processed the signals and displayed output decisions based on the gesture that was performed. Several tests were performed on one-human subject to identify multiple single-handed and two-handed gestures to recognize various word as well as basic sentences in the ASL. The experimental results were recorded and discussed.


Abstract In this talk we will look into the state of the art in sign language recognition to enable us sketch the requirements for future research that is needed. The system enables realtime hand gesture. Sornalatha Published 2014 in our country around 2.78 of peoples are not able to speak dumb. To discriminate between different hand gestures we have flex and inertial sensors embedded into the glove so that the three most important parameters i.e. movement of hands such as the Vietnamese sign language system. Authors design an algorithm to convert dynamic gesture to text at real time.5.


Deaf Hand

recognition method which is based on hand detection followed by hand tracking and then hand is segmented using motion and. Implementation of realtime static hand gesture recognition using artificial . Using gesture recognition we compute the mathematical interpretation of human hand gestures to recognize the signs conveyed by American Sign Language. Buy RealTime Hand Gesture Recognition Sign Language to Voice Conversion by Amalaraj Alex Vijay Raj Zhang Xiaorong online on Amazon.ae at best prices. the camera or there are occlusions the system can transform it to reconstruct the. converts hand gestures from the American Sign LanguageASL to voice signals. This article presents HandTalk a smart glove that can recognize basic hand gestures and convert them into speech using lowcost commercial offtheshelf COTS components. Through sign language communication is possible for a deafmute person without the means of acoustic sounds. The proposed method achieves a word recognition accuracy of 98.9. shivanisinghal487gmail.com. We designed a real time sign language recognition system that can recognize gestures of sign language from videos under complex backgrounds. The purpose of this is to improve the accuracy of classification.


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