Gesture Recognition for Speech Impaired Individuals
Ms.Aishwarya.S.Khutwad, Trupti Jadhav, Shruti Jadhav, Vaibhav Nagare, Yash Bhole
1. Ms. Aishwarya.S.Khutwad, lecturer, Information Technology, Mahavir Polytechnic, Nashik
2. Ms. Trupti Jadhav, Student, Information technology, Mahavir Polytechnic, Nashik
3. Ms.. Shruti Jadhav , Student, Information technology, Mahavir Polytechnic, Nashik
4. Mr. Vaibhav Nagare, Student, Information technology, Mahavir Polytechnic, Nashik
5. Mr.Yash Bhole, Student , Information technology, Mahavir Polytechnic, Nashik
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Abstract - Hand gesture recognition is a natural way of human computer interaction and an area of very active research in computer vision and machine learning. So, the primary goal of gesture recognition research applied to Human-Computer Interaction (HCI) is to create systems, which can identify specific human gestures and use them to convey information or controlling devices. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time.
This paper presents a solution, generic enough, with the help of machine learning algorithms, allowing its application in a wide range of human-computer interfaces, for real-time gesture recognition. Experiments carried out showed that the system was able to achieve an accuracy of 99.4% in terms of hand posture recognition and an average accuracy of 93.72% in terms of dynamic gesture recognition. It is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system.registrations.
Key Words: Real-Time Gesture Recognition, Human-Computer Interaction (HCI), Vision-Based Interfaces, Machine Learning Algorithms, Sign Language Recognition, Referee Command Language System (Recluse).
