Design and Implementation of an Innovative System for Automatic Recognition of ASL using Machine Learning
Grade Level at Time of Presentation
Senior
Major
Computer Science General Area
Institution
Morehead State University
KY House District #
99
KY Senate District #
27
Faculty Advisor/ Mentor
Sherif Rashad
Department
Department of Computer Science & Information Systems
Abstract
Design and Implementation of an Innovative System for Automatic Recognition of ASL using Machine Learning
Joshua Webb (undergraduate student researcher) and Sherif Rashad (faculty mentor)
Department of Computer Science & Information Systems
Deaf and hearing-impaired persons learn American Sign Language (ASL) as their natural language. There is a need for a new innovative technology that will enable deaf and hearing-impaired persons to communicate without difficulty, anytime and anywhere with persons who do not know ASL. We explore in this research project the problem of automatic conversion from ASL to speech using motion sensors and machine learning. The goal of this project is to design a smart system to capture and recognize hand gestures using leap motion sensors and machine learning algorithms. The new proposed system will be able to work in an adaptive way to learn new signs to expand and to improve the dictionary of ASL. This system will have a wide range of applications for healthcare, education, gamification, entertainment, and many other applications.
Design and Implementation of an Innovative System for Automatic Recognition of ASL using Machine Learning
Design and Implementation of an Innovative System for Automatic Recognition of ASL using Machine Learning
Joshua Webb (undergraduate student researcher) and Sherif Rashad (faculty mentor)
Department of Computer Science & Information Systems
Deaf and hearing-impaired persons learn American Sign Language (ASL) as their natural language. There is a need for a new innovative technology that will enable deaf and hearing-impaired persons to communicate without difficulty, anytime and anywhere with persons who do not know ASL. We explore in this research project the problem of automatic conversion from ASL to speech using motion sensors and machine learning. The goal of this project is to design a smart system to capture and recognize hand gestures using leap motion sensors and machine learning algorithms. The new proposed system will be able to work in an adaptive way to learn new signs to expand and to improve the dictionary of ASL. This system will have a wide range of applications for healthcare, education, gamification, entertainment, and many other applications.