SIGN LANGUAGE INTERPRETER USING MACHINE LEARNING

A u t h o r e t a l INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 98 INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 SIGN LANGUAGE INTERPRETER USING MACHINE LEARNING Dan Kupatsa BACHELOR OF SCIENCE IN COMPUTER SCIENCE DMI ST JOHN THE BAPTIST UNIVERSITY SCHOOL OF COMPUTER SCIENCE LILONGWE, MALAWI Abstract Sign language interpreters play a crucial role in facilitating communication between deaf or hard-of-hearing individuals and those who do not use sign language. Their work extends across various settings, including educational institutions, healthcare facilities, legal proceedings, and public events. This abstract explores the importance of sign language interpretation, highlighting the linguistic, cultural, and ethical considerations involved. Additionally, it examines the skills required for effective interpretation, such as fluency in sign language, cognitive processing speed, and adaptability in diverse environments. The document also discusses challenges interpreters face, including maintaining accuracy, conveying emotions, and ensuring inclusivity. As society moves toward greater accessibility, the role of sign language interpreters remains indispensable in fostering equal communication opportunities for all. CHAPTER I INTRODUCTION 1.1 Background of study A background study on sign language interpretation typically explores the historical development, significance, and current practices within the field. Here‘s an overview: 1. Historical Context: Sign languages have been in use for centuries, evolving within deaf communities worldwide. The formalization of sign language interpretation gained prominence in the 20th century, particularly with increased advocacy for accessibility and equal communication rights. 2. Importance of Sign Language Interpretation: INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 99 Sign language interpreters play a vital role in bridging communication gaps between deaf individuals and the hearing population. Their work ensures inclusivity across various sectors, including education, healthcare, legal proceedings, and public services. 3. Linguistic and Cultural Considerations: Sign languages are distinct, fully developed languages with their own grammar, syntax, and regional variations. Interpreters must be proficient in sign language and understand cultural nuances to ensure effective and respectful communication. 4. Skills and Training: Sign language interpreters require extensive training, including language proficiency, cognitive processing speed, and ethical considerations. Many countries have certification programs to ensure high standards of interpretation. 5. Challenges and Future Directions: Interpreters often face challenges such as maintaining accuracy in real-time translation, conveying emotions, and adapting to different contexts. The integration of technology, such as AI-assisted interpretation and video relay services, is shaping the future of the profession. 1.2 OBJECTIVES 1.To Analyze the Role of Sign Language Interpreters – Examine their impact on communication accessibility in various settings, such as education, healthcare, and legal proceedings. 2.To Identify Key Skills Required for Effective Interpretation – Investigate the linguistic proficiency, cognitive abilities, and ethical considerations necessary for interpreters to perform their duties effectively. 3. To Explore Challenges Faced by Sign Language Interpreters – Assess difficulties such as maintaining accuracy, conveying emotions, and adapting to different communication contexts. 4.To Evaluate the Importance of Cultural Sensitivity – Understand how cultural nuances influence sign language interpretation and its effectiveness in diverse communities. 5.To Examine Technological Advancements in Interpretation – Investigate the role of AI-assisted interpretation, video relay services, and digital platforms in shaping the future of sign language interpretation. 6.To Propose Strategies for Improving Accessibility– Develop recommendations to enhance interpreter training, certification, and integration into various professional fields. 1.3 SYSTEM DESCRIPTION 1. System Overview The sign language interpreting system facilitates communication between individuals who use sign language and those who rely on spoken or written language. It ensures accessibility in various environments, such as education, healthcare, legal settings, and public services. 2. Components of the System INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 100 - Human Interpreters: Trained professionals fluent in sign language who interpret conversations in real time. - Technological Tools: Video relay services (VRS), AI-powered interpretation software, and real-time captioning. - Input & Output Modalities: Sign language, spoken language, text-based communication, and facial expressions for contextual meaning. 3. Interpretation Process 1.Recognition: The interpreter or system captures the sign language input via visual observation or motion sensors. 2.Processing & Translation: Converts sign language into spoken or written language (or vice versa) while maintaining accuracy, emotional intent, and cultural nuances. 3.Delivery: The interpreted message is conveyed either through speech, text, or sign language, depending on the recipient‘s needs. 4. Challenges & Considerations - Accuracy & Speed: Ensuring real-time interpretation without losing meaning. - Cultural Sensitivity: Understanding different dialects and contextual expressions. - Technology Limitations: AI interpretation is evolving but not yet as nuanced as human interpreters. - User Accessibility: Systems must be designed to accommodate diverse needs, including different sign language variants and regional adaptations. 5. Future Enhancements - AI-powered gesture recognition to improve interpretation accuracy. - Wearable devices that provide real-time language translation. - Integration with augmented reality (AR) for immersive communication experiences. 1.4 LITERATURE REVIEW 1. Introduction The literature review provides an overview of academic and professional research on sign language interpretation. It explores historical developments, linguistic theories, challenges, and innovations that shape the profession today. 2. Historical Perspectives - Early documentation of sign languages, such as studies on American Sign Language (ASL), British Sign Language (BSL), and other regional sign languages. - The recognition of sign languages as fully developed languages with unique grammar and syntax. INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 101 - Growth of interpreter certification programs and accessibility regulations over time. 3. Linguistic & Cognitive Frameworks - Studies on the cognitive processing required for simultaneous interpretation. - Research on how sign languages differ structurally from spoken languages. - The role of facial expressions and non-manual markers in sign language communication. 4. Challenges in Interpretation - Accuracy in real-time translation and the risk of misinterpretation. - The impact of dialectal variations and regional sign language differences. - Emotional and cultural considerations when interpreting sensitive topics. 5. Technological Advancements - Use of Artificial Intelligence (AI) for automated sign language translation. - Development of wearable devices and motion-sensing gloves for sign recognition. - Video relay services (VRS) improving remote accessibility for deaf individuals. 6. Future Directions & Research Gaps - The need for improved AI-assisted interpretation that captures nuances of human emotion and dialectical differences. - More research on interpreter training methods to enhance cognitive speed and accuracy. - The exploration of immersive technologies like augmented reality (AR) in facilitating sign language interpretation. CHAPTER II SYSTEM ANALYSIS 2.1 Introduction System analysis plays a critical role in evaluating the effectiveness, efficiency, and adaptability of sign language interpretation systems. It involves examining various components, including human interpreters, technological tools, communication processes, and accessibility mechanisms. Through system analysis, researchers and developers can identify challenges, optimize existing methods, and propose enhancements for better communication between deaf and hearing individuals. This analysis explores key aspects such as linguistic accuracy, real-time processing, cultural considerations, and technological advancements, including AI-powered interpretation and video relay services. By understanding the interaction between interpreters, users, and assistive technologies, system analysis aims to improve accessibility and inclusivity in diverse environments. INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 102 2.2 PROBLEM DEFINITION Effective communication between deaf or hard-of-hearing individuals and those who do not use sign language is crucial for accessibility and inclusivity. Despite advancements in sign language interpretation, several challenges persist, including accuracy, real-time translation, cultural sensitivity, and technological limitations. The problem arises due to the complexity of sign languages, which involve distinct grammatical structures, facial expressions, and regional variations. Human interpreters face difficulties in maintaining speed, precision, and emotional nuance, while automated systems struggle with full linguistic and contextual comprehension. Additionally, accessibility gaps remain in education, healthcare, and legal settings, limiting equal opportunities for deaf individuals. This study aims to analyze existing interpretation methods, identify shortcomings, and explore potential solutions, such as improved interpreter training, AI-powered interpretation tools, and integrated technological frameworks to enhance communication equity. 2.3 EXISTING SYSTEM The current systems for sign language interpretation rely on two primary approaches: human interpreters and technological solutions. These methods enable communication between deaf individuals and those who do not use sign language, but each has its own strengths and limitations. 1. Human Interpretation Professional Sign Language Interpreters: Trained individuals who interpret spoken language into sign language and vice versa in real-time. Video Relay Services (VRS): Remote interpretation services where users connect with interpreters via video calls. Limitations: Availability of interpreters, interpretation accuracy in specialized fields (e.g., medical or legal contexts), and fatigue in real-time communication. 2. Technological Solutions AI-Based Sign Language Translators: Machine learning models trained to recognize signs and translate them into text or speech. Motion-Sensing Devices: Wearable technology and gloves equipped with sensors to capture hand movements and translate them into words. Automated Captions: Speech recognition software that transcribes spoken content in real time to aid communication. Limitations: AI struggles with complex grammar, facial expressions, and emotional nuances critical to sign language communication. 3. Challenges in the Existing System Accuracy Issues: AI-based solutions have difficulty understanding regional sign language variations. INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 103 Limited Accessibility: Many public institutions lack adequate interpretation services. Cultural Sensitivity: Interpreters must grasp context, tone, and cultural nuances to ensure meaningful communication. 2.4 FEASIBILITY STUDY A feasibility study evaluates the practicality and viability of implementing or improving a sign language interpretation system. It considers technical, economic, legal, operational, and scheduling aspects to determine if the system can be successfully developed and sustained. 1. Technical Feasibility Technology Availability: Assessing existing tools like AI-powered interpretation, motionsensing devices, and video relay services. System Integration: Compatibility with various platforms (e.g., healthcare, education, and legal institutions). Challenges: AI‘s ability to recognize gestures, facial expressions, and emotional nuances accurately. 2. Economic Feasibility Cost of Implementation: Evaluating expenses for interpreter training, technology acquisition, and system maintenance. Funding Sources: Government grants, corporate sponsorships, and non-profit initiatives. Return on Investment (ROI): The long-term benefits of improved communication accessibility. 3. Legal Feasibility Compliance with Accessibility Laws: Ensuring the system meets disability rights and communication accessibility regulations. Data Privacy Concerns: Addressing security issues in AI-based interpretation services. Standardization: Developing universal guidelines for sign language translation systems. 4. Operational Feasibility User Adoption: Evaluating how deaf individuals, interpreters, and organizations will interact with the system. Training Requirements: Preparing professionals to use advanced interpretation tools effectively. Scalability: Ensuring the system can expand to meet growing demand. 5. Scheduling Feasibility Project Timeline: Estimating the duration required for research, development, testing, and deployment. Milestones & Deliverables: Setting achievable goals within realistic timeframes. INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 104 2.5 PROPOSED SYSTEM System Components 1. Gesture Recognition Module - Utilizes cameras or sensors to capture hand movements, facial expressions, and body gestures. - Machine learning models trained to recognize various signs in different sign language dialects. 2. Translation Engine - Converts recognized signs into spoken or written language. - Can include contextual understanding for improved accuracy. 3. Speech-to-Sign Module - Converts spoken or written language into sign language animations or holographic representations. - Offers real-time feedback to ensure fluid communication. 4. User Interface - Intuitive interface for both signers and non-signers. - Could include mobile, web, or wearable devices for accessibility. 5. Cloud-based Processing & AI Training - Continuous learning to improve accuracy over time. - Cloud-based storage allows for updates and expansion of vocabulary. 2.6 SYSTEM OBJECTIVE The objective of a Sign Language Interpreter System is to facilitate seamless communication between sign language users and non-signers by leveraging technology. Here are the key objectives: 1. Accessibility & Inclusivity - Ensure deaf and hard-of-hearing individuals can communicate effectively with non-signers. - Enable broader participation in education, workplaces, healthcare, and public services. 2. Real-time Translation - Provide instant conversion of sign language into text or speech. - Allow spoken or written language to be transformed into sign language animations. 3. Accuracy & Context Awareness - Employ AI to understand context, emotions, and nuances in sign language. INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 105 - Reduce translation errors by refining recognition models. 4. Multi-language Support - Accommodate different sign language variations (e.g., ASL, BSL, ISL). - Enable cross-language communication for international interactions. 5. Device Compatibility & User-Friendliness - Develop solutions for mobile, web, and wearable devices. - Ensure an intuitive and adaptive interface for ease of use. 2.7 SYSTEM SPECIFICATION 1. Hardware Requirements - Camera/Sensors: High-resolution cameras or depth sensors (e.g., LiDAR, infrared) for gesture recognition. - Processing Unit: Dedicated GPU (e.g., NVIDIA RTX series) or TPU for AI-driven real-time processing. - Microphone (for speech-to-sign module): High-quality noise-filtering microphone for voice input. - Display: Touch screen, wearable display (AR glasses), or holographic projector for sign visualization. - Storage: Cloud or local storage for learned sign language models and real-time data caching. 2. Software Requirements - Operating System: Windows, Linux, macOS, or mobile OS (Android, iOS) for crossplatform use. - Programming Languages: Python (TensorFlow, OpenCV), C++ (Computer Vision), JavaScript (Web Integration). - AI & ML Frameworks: TensorFlow, PyTorch, MediaPipe, OpenAI Whisper for sign recognition. - Natural Language Processing (NLP): Transformer-based models (GPT, BERT) for context aware translations. - Cloud Services: AWS, Google Cloud, Azure for model training and data processing. 3. Network & Connectivity - Internet: Cloud-based AI processing requires stable internet connection. - Bluetooth/Wi-Fi: For wearable device communication (e.g., smart gloves). - Edge Computing: Local AI processing for offline functionality. 4. Security & Privacy INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 106 - Data Encryption: Secure transmission of sign language data. - User Authentication: Biometric authentication for personalized settings. - Ethical AI Compliance: Fair and bias-free model training for inclusivity. CHAPTER III SYSTEM DESIGN 3.1 INTRODUCTION A Sign Language Interpreter System is designed to bridge communication between sign language users and non-signers using technology. The system employs artificial intelligence, computer vision, and natural language processing to recognize, translate, and generate sign language gestures. Purpose of the System The goal is to create an inclusive communication tool that provides real-time translation of sign language into text or speech and vice versa. This can be useful in various settings, including education, healthcare, customer service, and social interactions. Key Design Consideration 1.User-Centric Interface – The system should be intuitive and easy to use for both signers and non-signers. 2. Gesture Recognition – Accurate and efficient sign language detection using cameras or sensors. 3. Translation Accuracy– AI models must understand context, grammar, and variations in sign language. 4. Multi-platform Accessibility – Available on mobile, web, and wearable devices for convenience. 5. Privacy & Security – Ensuring user data and interactions are protected. 3.2 SYSTEM ARCHITECTURE System Architecture for a Sign Language Interpreter The architecture of a Sign Language Interpreter System consists of multiple components that work together to recognize, translate, and generate sign language gestures. Below is an overview of its layered architecture: 1. Input Layer (Gesture & Speech Capture) - Camera/Sensors – Captures hand movements, facial expressions, and body gestures. - Microphone – Records spoken language to convert into sign language. - Touch/Text Input – Allows users to enter text for sign translation. 2. Processing Layer (AI & NLP Model Execution) INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 113 Modules TensorFlow A library for dataflow and differentiable programming used for a variety of applications called TensorFlow is free and open source software. OpenCV A collection of programming functions with a focus on real-time computer vision is called OpenCV (Open Source Computer Vision collection).It was initially created by Intel, then backed by Willow Garage and Itseez (which Intel eventually purchased). Under the terms of the opensource BSD license, the library is free to use and cross-platform. Keras Python-based Keras is an open-source library for neural networks. It may function on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML, among other frameworks. It focuses on being user friendly, modular, and extendable in order to enable quick experimentation with deep neural networks. Its major inventor and maintainer is François Chollet, a Google engineer, and it was created as a component of the research effort of project ONEIROS (Openended Neuro-Electronic Intelligent Robot Operating System). The XCeption deep neural network model was also created by Chollet. NumPy A library for the Python programming language called NumPy adds support for big, multidimensional arrays and matrices as well as a ton of high-level mathematical operations that can be performed on these arrays. Jim Hugunin and a number of other developers worked together to produce Numeric, the predecessor to NumPy. By heavily altering Numeric and combining features from the rival Numarray, Travis Oliphant built NumPy in 2005. Numerous people contribute to NumPy, an opensource program. CHAPTER VI SYSTEM IMPLEMENTATION 6.1 INTRODUCTION A hand sign interpreter system is designed to bridge communication gaps between individuals who use sign language and those who do not. The implementation of such a system typically involves hardware components (like cameras or motion sensors) and software using machine learning or deep learning techniques to recognize and translate gestures into text or speech. The implementation of a hand sign interpreter system starts with understanding the user needs and selecting the appropriate technology stack. The main objectives are: 1. Capturing Hand Gestures – Using cameras or wearable sensors to record movements. 2. Processing Data – Applying image processing techniques to identify sign language gestures. INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 114 3. Recognition & Translation – Using trained AI models to classify signs and convert them into corresponding text or speech. 4. User Interface Design – Ensuring accessibility for both sign language users and non-sign language users. 6.2.2 CODING FIGURE 3.7: CODE FIGURE 3.8: CODE INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 115 FIGURE 3.9: CODE 6.2.1.1 FRONT END FIGURE 3.10: FROND END INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 116 6.4.1.2 BACKEND FIGURE 3.11: CODE FIGURE 3.12: CODE FIGURE 3.13: CODE INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 117 FIGURE 3.14: CODE INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 118 FIGURE 3.15: CODE CHAPTER VII CONCULSION & FUTURE ENHANCEMENTS 7.1 CONCULSION A sign language interpreter plays a crucial role in bridging communication between deaf or hard of hearing individuals and the hearing world. Their work ensures accessibility, inclusion, and equal participation in various settings—whether in education, healthcare, legal proceedings, or daily interactions. By interpreting spoken language into sign language and vice versa, they empower individuals to connect, understand, and express themselves fully. INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 119 Ultimately, sign language interpreters are not just language facilitators; they are advocates for accessibility and inclusion. Their presence strengthens communities, fosters understanding, and helps break communication barriers, ensuring that everyone has the opportunity to be heard and understood. 7.2 FUTURE ENHANCEMENTS Future enhancements for sign language interpretation could include AI-powered interpreters, improving accessibility through real-time digital translation. Advances in gesture recognition technology may enable more seamless communication, reducing the reliance on human interpreters in certain situations. Wearable technology, like smart gloves that detect sign language movements and convert them into text or speech, could provide an innovative solution. Improvements in **VR and AR** could create immersive learning experiences for sign language users, making education and training more interactive. Additionally, legislative advancements and broader awareness campaigns can ensure sign language interpretation becomes a standard in workplaces, education, and public services— strengthening inclusivity for the deaf and hard-of-hearing communities. References 1. Kumar, A., & Sharma, R. (2024). Sign language interpretation using machine learning and artificial intelligence. Neural Computing and Applications. https://link.springer.com/article/10.1007/s00521-02410395-9 2. Singh, P., & Kaur, G. (2023). Interpretation of Sign Language Using Machine Learning. IEEE Xplore. https://ieeexplore.ieee.org/document/10877588 3. Patel, H., & Desai, M. (2024). A Review on Sign Language Recognition System using Machine Learning. International Journal of Research and Analytical Reviews (IJRAR). https://ijrar.org/papers/IJRAR24B1244.pdf 4. Alonso, J. M., & Martinez, R. (2021). Artificial Intelligence Technologies for Sign Language. Sensors, 21(17), 5843. https://www.mdpi.com/1424-8220/21/17/5843 5. Choudhury, S., & Banerjee, A. (2025). Advancements in Machine Learning Techniques for Hand Gesture-Based Sign Language Recognition. Archives of Computational Methods in Engineering. https://link.springer.com/article/10.1007/s11831-025-10258-z INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 120 6. Ahmed, T., & Malik, S. (2025). Real-Time Gesture Recognition for Sign Language Using Machine Learning. IEEE Xplore. https://ieeexplore.ieee.org/document/10871766 7. Rao, V., & Pillai, M. (2023). A Survey of Advancements in Real-Time Sign Language Translators: 8. Integration with IoT Technology. Technologies, 11(4), 83. https://www.mdpi.com/2227-7080/11/4/83 Mwale, C., & Banda, T. (2025). Challenges and Innovative Solutions in Sign Language Communication. 9. Journal of Emerging Technologies and Innovative Research, 12(6), 828–835. 10. https://www.jetir.org/papers/JETIR2506828.pdf 11. Signapse.ai. (2025). Signapse: AI Sign Language Translator for ASL & BSL. https://www.signapse.ai/ 12. Royal Academy of Engineering. (2025). Terp360: Real-Time Speech-to-Sign Language App Using 3D Avatars. https://www.yahoo.com/news/articles/app-translates-speechsign-language-111731164.html 13. Rastogi, U., Mahapatra, R. P., & Kumar, S. (2025). Advancements in machine learning techniques for hand gesture-based sign language recognition: A comprehensive review. Archives of Computational Methods in Engineering, 32, 4265–4302. https://link.springer.com/article/10.1007/s11831-025-10258-z 14. Papatsimouli, M., Sarigiannidis, P., &Fragulis, G. F. (2023). A survey of advancements in realtime sign language translators: Integration with IoT technology. Technologies, 11(4), 83. 15. https://www.mdpi.com/2227-7080/11/4/83 16. Savatia, E. (2025). Terp360: Real-time speech-to-sign language app using 3D avatars. Royal Academy of Engineering. https://www.yahoo.com/news/articles/app-translates-speech-signlanguage111731164.html 17. Signapse.ai. (2025). Addressing the challenges of sign language interpretation. 18. https://www.signapse.ai/post/addressing-the-challenges-of-sign-languageinterpretation 19. MLTMS. (2025). Sign language interpretation in the digital age: Challenges and innovations. https://www.mltms.org/sign-language-interpretation-in-the-digital-agechallenges-andinnovations/ 20. Kara Technologies. (2025). How we translate sign language with AI support in only 3 steps. 21. https://www.kara.tech/post/applied-ai-sign-language-translation 22. Grasp.info. (2025). Sign language translator – Free online AI tool. https://grasp.info/tools/signlanguage-translator INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS www.ijrcar.com Vol.13 Issue 11, Pg.: 98-121 November 2025 D a n K u p a t s a Page 121 23. Simple Science. (2025). Advancements in sign language translation technology. 24. https://scisimple.com/en/articles/2025-08-18-advancements-in-sign-languagetranslationtechnology--a3dde11 25. Nagish. (2025). Nagish acquires sign.mt to drive research in AI and sign language. https://finance.yahoo.com/news/nagish-acquires-sign-mt-drive-130000135.html 26. GitHub Contributors. (2024). Sign-language-recognition-system. GitHub. 27. https://github.com/topics/sign-language-recognition-system