Real-Time Implementation of an AI-Based Virtual Sign Language Recognition and Interpretation System

EasyChair Preprint 10152

Abstract

As we step further into the age of AI, its application is blossoming across numerous sectors. We, as researchers, have the opportunity to harness this technology to address a pressing issue: the inclusion of deaf individuals, particularly in accessing healthcare services. An intriguing prospect is the creation of a real-time, virtual sign language reader and interpreter.

This paper delves into the need, viability, and potential benefits of such a technological innovation.

Envisioned to be powered by machine learning algorithms, this virtual sign language interpreter would be capable of recognizing and interpreting a broad spectrum of sign language gestures and movements in real time. The beauty of machine learning is its capacity to learn from its errors and adapt over time, enhancing its ability to decipher even the most complex sign language expressions. Additionally, we plan to incorporate data mining techniques to sift through large volumes of sign language data, identifying patterns to further refine the interpreter's recognition abilities.

Accessibility is a key design principle for this tool. It will be compatible with a diverse array of devices and interfaces and will be designed to integrate seamlessly into existing healthcare systems and workflows.

The development of a real-time, virtual sign language reader and interpreter, powered by AI technologies, offers a promising avenue for enhancing healthcare service access and quality for deaf patients. By enabling more effective communication between healthcare providers and patients, we can significantly improve health outcomes for the deaf community.

By investing our efforts in research and development in this area, we are working towards creating a healthcare system that is truly inclusive and accessible to all.

Keyphrases: Accessibility, Artificial Intelligence (AI), Clustering, Data Mining, Data Science, Deaf community, Healthcare providers, Image and video recognition, Principal Component Analysis (PCA), Real-time interpretation, Virtual sign language interpreter, communication, deep learning, health outcomes, machine learning

@booklet{EasyChair:10152,
  author    = {Aicha Zizoune and Asmae Zizoune and Roba Hamdaoui and Karima Salaheddine and Anouar Riadsolh and Soumia Ziti},
  title     = {Real-Time Implementation of an AI-Based Virtual Sign Language Recognition and Interpretation System},
  howpublished = {EasyChair Preprint 10152},
  year      = {EasyChair, 2023}}