Review of Sign Language Detection Using Flutter and TensorFlow
Mst. Harsh Bhingarde
Finolex Academy of Management and Technology, Ratnagiri.
University of Mumbai
harshbhingarde749@gmail.com
Mst. Sujal Jadhav
Finolex Academy of Management and Technology, Ratnagiri.
University of Mumbai
sujaldeepakjadhav09@gmail.com
Mst. Kaiwalya Surve
Finolex Academy of Management and Technology, Ratnagiri.
University of Mumbai
kaiwalyasurve9403@gmail.com
Mst. Ayush Bansod
Finolex Academy of Management and Technology, Ratnagiri.
University of Mumbai
ayush09bansod@gmail.com
Prof.M.M.Hatiskar
Assistant Professor, Finolex Academy of Management and Technology, Ratnagiri.
University of Mumbai
mrunmayee.hatiskar@famt.ac.in
Abstract—Sign language is an indispensable mode of communication for individuals who are deaf or hard of hearing. However, the majority of the global population does not understand sign language, creating a communication barrier between the hearing-impaired community and others. This paper presents a review of current methodologies and technologies developed for sign language detection, with a particular focus on integrating Flutter and TensorFlow to enable real-time sign language recognition on mobile platforms. The use of Flutter, a cross-platform framework for mobile development, in conjunction with TensorFlow, an advanced machine learning framework, provides an innovative solution to efficiently detect and translate signs into spoken language or text. The paper discusses existing approaches, identifying their strengths and limitations, and highlights the gaps that remain in achieving accurate, real-time sign detection. Through an analysis of current solutions, we propose an architecture that leverages the strengths of both Flutter and TensorFlow to overcome the challenges of hardware limitations, data availability, and processing efficiency. Furthermore, future directions for enhancing the system’s scalability, accuracy, and ease of use are discussed, with a focus on improving communication for the hearing-impaired community.
Keywords—Sign Language Detection, Flutter, TensorFlow, Machine Learning, Real-Time Recognition
