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Introduction

Neuromorphic computing is a rapidly growing field that draws inspiration from the biological brain to develop advanced computing systems. One of the most intriguing applications of neuromorphic computing is in real-time sign language translation, which has the potential to revolutionize communication for deaf individuals. This thesis aims to explore the use of neuromorphic computing for real-time sign language translation, with a focus on developing efficient and accurate systems that can bridge the communication gap between deaf and hearing individuals.

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Introduction to Neuromorphic Computing
2.2 Sign Language Translation Systems
2.3 Neural Networks in Sign Language Recognition
2.4 Challenges in Real-Time Sign Language Translation
2.5 Neuromorphic Chips and Hardware
2.6 Previous Studies on Neuromorphic Sign Language Translation
2.7 Machine Learning Algorithms for Sign Language Recognition
2.8 Deep Learning Models for Sign Language Translation
2.9 Human-Computer Interaction in Sign Language Translation
2.10 Current Trends in Neuromorphic Computing for Sign Language Translation

Chapter 3: Research Methodology
3.1 Overview of Research Design
3.2 Data Collection Methods
3.3 Data Preprocessing Techniques
3.4 Model Development and Training
3.5 Evaluation Metrics
3.6 Experimental Setup
3.7 Performance Analysis
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Accuracy of Neuromorphic Sign Language Translation System
4.2 Efficiency of Real-Time Translation
4.3 Comparison with Traditional Systems
4.4 User Experience and Feedback
4.5 Potential Improvements and Future Work
4.6 Impact on Communication for Deaf Individuals

Chapter 5: Conclusion and Summary
5.1 Recap of Research Objectives
5.2 Key Findings and Contributions
5.3 Implications for Future Research
5.4 Practical Applications and Benefits
5.5 Conclusion and Final Thoughts

Thesis Overview:

Sign language is a visual-gestural language used by deaf and hard-of-hearing individuals to communicate. The lack of understanding and proficiency in sign language among the general population creates a significant communication barrier for deaf individuals. Traditional methods of sign language translation, such as human interpreters or video-based systems, are often slow, costly, and not always readily available. The emerging field of neuromorphic computing offers a promising solution to this problem by leveraging the brain’s computing principles to develop efficient and real-time sign language translation systems.

This thesis aims to explore the use of neuromorphic computing for real-time sign language translation, with a focus on developing accurate and efficient systems that can bridge the communication gap between deaf and hearing individuals. The study will begin with an introduction to neuromorphic computing and its application in sign language translation, followed by a comprehensive review of the existing literature on the topic. The research methodology will detail the design, data collection, model development, and evaluation metrics used in the study. The discussion of findings will analyze the accuracy, efficiency, user experience, and potential improvements of the neuromorphic sign language translation system. Finally, the conclusion and summary will recap the research objectives, key findings, implications for future research, and practical applications and benefits of the study.

Overall, this thesis aims to contribute to the growing body of knowledge on neuromorphic computing and its potential in improving communication accessibility for deaf individuals. By developing a real-time sign language translation system, this research seeks to empower deaf individuals to participate more fully in social, educational, and professional contexts.

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