Abstract

This proposal outlines a final year project in computer science aimed at developing a sign language detection system utilizing artificial intelligence (AI) and a mobile application. The project addresses the need for accessible communication tools for individuals with hearing impairments by leveraging AI techniques for real-time sign language interpretation. Through the integration of computer vision and machine learning algorithms, the proposed system aims to accurately recognize and translate sign language gestures into text or speech, facilitating seamless communication between individuals with hearing impairments and those who do not understand sign language.

Introduction

Sign language serves as a primary mode of communication for millions of individuals with hearing impairments worldwide. However, the communication barrier between the deaf or hard of hearing community and individuals who do not understand sign language remains a significant challenge. Traditional solutions, such as human interpreters or video relay services, are often limited by factors such as availability, cost, and accessibility. This project seeks to address these limitations by developing a mobile application that harnesses the power of AI to detect and interpret sign language gestures in real-time, thereby enabling more inclusive and accessible communication experiences.

Problem

The communication gap between individuals with hearing impairments and the broader community poses significant challenges in various social, educational, and professional contexts. Existing solutions, such as manual interpretation or text-based communication, are often inefficient, time-consuming, and prone to misinterpretation. Moreover, the reliance on external services or devices limits the autonomy and independence of individuals with hearing impairments. Thus, there is a pressing need for a more efficient and accessible solution that empowers individuals with hearing impairments to communicate effectively in diverse settings.

Aim

The primary aim of this project is to develop a sign language detection system integrated into a mobile application, enabling real-time interpretation of sign language gestures. By harnessing the capabilities of AI and computer vision, the project seeks to provide an intuitive and accessible communication tool for individuals with hearing impairments. The ultimate goal is to bridge the communication gap between the deaf or hard of hearing community and individuals who do not understand sign language, promoting inclusivity and equal access to information and services.

Objectives

1. To design and develop a mobile application with an intuitive user interface for sign language detection and interpretation.

2. To collect and curate a comprehensive dataset of sign language gestures encompassing a wide range of expressions and variations.

3. To implement and train machine learning models for sign language recognition using computer vision techniques.

4. To integrate the sign language detection system with real-time video processing capabilities on mobile devices.

5. To evaluate the performance and accuracy of the system through rigorous testing with diverse datasets and user feedback.

6. To optimize the system for efficiency, reliability, and accessibility, ensuring seamless integration into everyday communication scenarios.

Research

The proposed research will draw upon interdisciplinary techniques from computer vision, machine learning, and mobile app development. The initial phase will involve data collection and preprocessing, including the acquisition of sign language videos and annotations for training and validation purposes. Subsequently, machine learning models, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), will be explored and implemented to recognize sign language gestures from video inputs. The development of the mobile application will involve integrating the trained models with real-time video processing functionalities, ensuring low-latency performance on mobile devices. Evaluation of the system’s accuracy and usability will be conducted through user studies and performance metrics, with iterative refinements based on feedback and empirical results. Additionally, ethical considerations, such as privacy and accessibility, will be carefully addressed throughout the research process to ensure the inclusivity and integrity of the sign language detection system.