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Introduction

Chapter 1: Introduction
1.1 Background of study
1.2 Problem Statement
1.3 Objective of study
1.4 Limitation of study
1.5 Scope of study
1.6 Significance of study
1.7 Structure of the Thesis
1.8 Definition of terms

Chapter 2: Literature Review
2.1 Introduction to Computer Vision
2.2 Sign Language Recognition
2.3 Previous Work on Sign Language Interpretation
2.4 Machine Learning in Sign Language Interpretation
2.5 Challenges in Sign Language Interpretation
2.6 Image Processing Techniques
2.7 Deep Learning for Computer Vision
2.8 Gesture Recognition Methods
2.9 Sign Language Databases
2.10 Evaluation Metrics for Sign Language Interpretation

Chapter 3: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Feature Extraction Techniques
3.4 Machine Learning Algorithms Selection
3.5 Training and Testing Models
3.6 Model Evaluation
3.7 Real-time Sign Language Interpretation
3.8 User Interface Design

Chapter 4: System Implementation
4.1 Software Development Tools
4.2 Coding and Implementation
4.3 Database Integration
4.4 Testing and Debugging
4.5 Performance Optimization
4.6 User Testing
4.7 System Maintenance
4.8 Ethical Considerations

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Achievements of the Study
5.3 Recommendations for Future Research
5.4 Conclusion

Thesis Overview:

Computer vision has made significant advancements in recent years, with applications ranging from object recognition to facial expression analysis. In the domain of sign language interpretation, computer vision plays a crucial role in bridging communication gaps between individuals who are deaf or hard of hearing and those who are hearing. This thesis focuses on the development of a computer vision system for sign language interpretation, aiming to enhance the accuracy and efficiency of real-time communication between these two groups.

Chapter 1 provides an introduction to the research topic, discussing the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 presents a comprehensive review of relevant literature on computer vision, sign language recognition, machine learning, image processing, and gesture recognition methods. The chapter also covers challenges in sign language interpretation and evaluation metrics for assessing performance.

Chapter 3 details the system design and methodology, outlining the system architecture, data collection, preprocessing, feature extraction, machine learning algorithms, model training, evaluation, real-time interpretation, and user interface design. Chapter 4 discusses the implementation of the system, including software development tools, coding, database integration, testing, performance optimization, user testing, system maintenance, and ethical considerations.

Finally, Chapter 5 concludes the thesis, summarizing the findings, achievements, recommendations for future research, and overall conclusion. The thesis aims to contribute to the field of sign language interpretation by developing a reliable and efficient computer vision system that can facilitate seamless communication between individuals with different communication needs.

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