International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
SignSense – Sign Language Detection and Translation Software Prof. Rahul Laxman Thorat1, Atharva Kadam2, Aditya Arkasali3, Makarand Warade4 1Professor, Dept. of Computer Engineering, TSSM Bhivarabai Sawant College of Engineering & Research Pune, India 2 Dept. of Computer Engineering, TSSM Bhivarabai Sawant College of Engineering & Research Pune, India 3Dept. of Computer Engineering, TSSM Bhivarabai Sawant College of Engineering & Research Pune, India
---------------------------------------------------------------------***--------------------------------------------------------------------and the development of machine learning models for real- time gesture recognition.
Abstract - Sign language is a vital means of
communication for millions of individuals worldwide, particularly those who are deaf or hard of hearing. This software, aptly named SignSense, leverages cutting-edge computer vision and machine learning techniques to detect and translate sign language gestures into text and in real time. SignSense's core innovation lies in its ability to bridge the communication gap between the hearing and non-hearing communities. By harnessing the power of computer vision, it accurately interprets the intricate movements and expressions of sign language, transforming them into clear, concise text. Moreover, SignSense can be integrated into various platforms and devices, opening up endless possibilities for communication, from educational settings to daily interactions.
Following the research phase, the project will shift its focus to the development of the software's core components, including the sign language detection module, translation to text, and speech synthesis functionality. The software will be designed to run on multiple platforms, making it accessible to a wide range of users. The project's scope also extends to advocacy and awareness initiatives aimed at promoting the adoption of SignSense in educational institutions, workplaces, and public spaces. This holistic approach underscores the project's ambition to create a more inclusive society by breaking down the communication barriers faced by the deaf and hard of hearing communities. Sign language recognition is an area of research that involves pattern matching, deep learning, computer vision, natural language processing, and a design module or algorithm to identify sign language. It can be extended further to human-computer interaction without a voice interface. This system belongs to multidisciplinary content and the approach can be considered as a part of the Sign Language System. [1]
Index Terms: Sign Language, Translation, ASL, Deep Learning. 1. INTRODUCTION The primary aim of the SignSense project is to develop an advanced sign language detection and translation software that enhances accessibility and communication for individuals who are deaf or hard of hearing, ultimately fostering a more inclusive society. SignSense, a sign language detection and translation software, is born out of a deeply rooted commitment to address the communication barriers faced by individuals who are deaf or hard of hearing. The motivation behind this project stems from a profound understanding of the significance of inclusivity, as we aim to create a world where everyone can participate in conversations, education, and various facets of daily life without any restrictions.
1.2 Outcomes The SignSense project anticipates a range of significant outcomes that will contribute to its overarching mission of enhancing communication and accessibility for individuals who are deaf or hard of hearing. These outcomes include: Effective Sign Language Communication: SignSense will empower individuals who use sign language to communicate effectively with both the hearing and non-hearing communities, reducing the communication gap and fostering greater inclusivity. Improved Education Access: The software's integration into educational settings will enable students who are deaf or hard of hearing to access classroom discussions and educational resources in real time, thereby improving their educational outcomes.
1.1 Scope of Work The scope of work for the SignSense project encompasses a multifaceted approach that aims to achieve a comprehensive and groundbreaking sign language detection and translation solution. The project will commence with in-depth research into sign language linguistics and recognition methodologies, laying the foundation for a robust sign language gesture detection system. This will involve the acquisition of a diverse dataset of sign language gestures, their annotation,
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Enhanced Workplace Inclusivity: By providing a tool for clear communication, SignSense will facilitate the inclusion of individuals with hearing impairments in the workplace, expanding their employment opportunities and contributing to diverse and productive work environments.
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