TFOOD (TensorFlow Object Detection) Application Design Smart Solution to Create Digital Communication Tools for People with Deaf and Speech Disabilities

Abstract

Advances in technology have paved the way for innovative solutions aimed at improving the
well-being of the lives of deaf and speech-impaired individuals. This paper presents the design
of TFOOD (TensorFlow Object Detection), a smart application designed to facilitate digital
communication for deaf and speech impaired people. Utilizing TensorFlow object detection
technology, TFOOD translates visual cues, such as SIBI and BISINDO sign language cues,
into text or audio output that is easily understood by others. Prototype of “TFOOD” application
which is designed to help reduce the difficulties faced by people with hearing and speech
impairments. These applications involve training sophisticated models on diverse data sets
and optimizing algorithms to ensure high accuracy and responsiveness in real-world
scenarios. Results show that TFOOD significantly increases communication accessibility for
deaf and speech-impaired individuals, providing an effective tool for interacting in a variety of
social and professional contexts. This paper also discusses the challenges faced during
implementation, including model accuracy and integration, and discusses potential future
developments to further improve the system's capabilities and accessibility. Through this
exploration, TFOOD demonstrates its contribution to digital inclusion and offers insight into
the development of supporting communications technologies.