AI Tool/Mobile App for Indian Sign Language (ISL)

Abstract

This project proposes an AI-powered Sign Language Generator for Audio-Visual Content in English/Hindi that leverages cutting-edge technologies to bridge this communication gap. The system captures spoken language using advanced speech recognition techniques provided by the Google Speech Recognition API, transcribing speech into text with high accuracy. When inputs are in Hindi, the system employs the Google Translate API to convert the text into English, ensuring a standardized vocabulary that maps to ISL gestures.

Introduction

1. Motivation

Millions in India rely on Indian Sign Language (ISL), but a communication gap exists between ISL users and non-signers, limiting access to education, healthcare, and employment. Despite tech advancements, real-time spoken-to-ISL translation remains unavailable.

2. Problem Statement

Speech-to-text and text-to-speech technologies are common, but India lacks an accessible, real-time spoken language to ISL system. Human interpreters are scarce, expensive, and often unavailable in rural areas.

3. Project Objective

Develop an AI-powered system that translates spoken language (English/Hindi) into ISL videos in real time using speech recognition, natural language processing (NLP), and a pre-trained ISL gesture database.

4. Project Scope

The system will include:

• Real-time speech-to-text

• Text-to-ISL translation

• ISL video generation

• Support for English and Hindi

• Multimedia integration for better accessibility

5. Project Introduction

This AI-driven project addresses accessibility for the deaf community through real-time spoken-to-ISL video translation. Core modules:

1. Speech Recognition – Captures and converts spoken language to text.

2. NLP – Processes text for accurate ISL translation.

3. Sign Mapping – Retrieves ISL gestures from a database.

4. Video Generation – Produces coherent ISL gesture videos.

6. Related Work (Literature Review)

• Sharma & Gupta (2022): AI models (CNNs, RNNs) enhance gesture recognition but face challenges in real-time processing and dataset limitations.

• Patel & Verma (2021): Real-time translation using ASR and SLG, but issues exist with animation fluidity and linguistic differences.

• Iyer & Das (2020): Deep learning (CNNs, LSTMs) improves gesture recognition; recommends combining vision with sensor-based data.

• Mehta & Reddy (2019): Use of virtual avatars for ISL communication; realism and facial expression accuracy are current hurdles.

• Rao & Sen (2018): NLP and AI for multilingual sign translation; emphasizes structural differences in sign languages and the need for large datasets.

Conclusion: AI has potential to bridge communication gaps but still needs better data, fluid animations, and real-time processing.

7. Methodology

A. Requirements Analysis

• Collect ISL gesture data and user feedback

• Define system components and constraints for real-time, mobile-friendly performance

B. System Design

• Modular architecture with speech-to-text, NLP, ISL grammar, and animation engines

• Use of LSTM, Transformers, or GANs for gesture synthesis

• UI/UX for seamless interaction

C. Development

• Implement speech recognition (Google API/custom ASR)

• NLP restructuring (rule-based + BERT/GPT models)

• Gesture generation (CNN + LSTM, GANs, OpenPose)

• 3D animated avatars and real-time APIs (e.g., WebSockets)

D. Testing

• Conduct unit, integration, accuracy, and user testing

• Ensure low-latency and real-time functionality

E. Deployment

• Web/mobile app release

• Cloud hosting (AWS/Firebase)

• Beta testing with deaf communities

F. Maintenance

• Regular model updates with new ISL data

• Bug fixes, performance tuning, and user-driven improvements

Conclusion

The detailed project timeline presents a strategic and well-structured approach to implementing the AI-powered Sign Language Generator. By dividing the development process into six clearly defined and manageable phases, the project ensures a systematic progression from research to final deployment. Each phase is designed with specific objectives and deliverables, facilitating focused efforts and measurable outcomes. Dependencies between different modules—such as the linkage between ASR, NLP, and animation—are carefully considered and planned to enable smooth integration. Sufficient time is allocated for critical stages like testing, system optimization, and UI refinement to guarantee functional accuracy and real-time responsiveness. The inclusion of a Gantt Chart enhances the planning process by providing a visual representation of the project timeline. It helps team members and stakeholders track progress, foresee delays or overlaps, and manage resources effectively. Overall, this structured workflow significantly contributes to the project’s successful and timely execution, ensuring that the final system meets high standards of functionality, quality, and user experience

References

[1] Kaur, J., & Kumar, P. (2021). \"Indian Sign Language Recognition Using Deep Learning Techniques.\"International Journal of Advanced Computer Science and Applications, 12(4), 187-195. [2] Sharma, R., & Gupta, A. (2020). \"AI-Based Sign Language Recognition System for Hearing Impaired Individuals.\"IEEE Transactions on Neural Networks and Learning Systems, 31(7), 2502-2511. [3] Patel, R., & Sharma, K. (2019). \"Speech-to-Sign Language Translation: A Deep Learning Approach.\"International Journal of Computational Linguistics Research, 10(2), 89-102. [4] Biswas, S., & Roy, A. (2022). \"Indian Sign Language Dataset for Machine Learning: Challenges and Opportunities.\"Springer Lecture Notes in Computer Science, 13567, 143-156. [5] Mishra, A., & Verma, P. (2021). \"A Comprehensive Survey on Sign Language Recognition Using AI.\"Journal of Artificial Intelligence Research, 58, 97-121. [6] Singh, M., & Das, R. (2020). \"Enhancing ISL Translation Accuracy Using NLP and Computer Vision.\"Proceedings of the International Conference on AI and Robotics, 67-74. [7] Jain, A., & Mehta, S. (2019). \"Real-Time Gesture Recognition for Indian Sign Language Using CNNs.\"Neural Computing and Applications, 32(3), 2113-2125. [8] Raj, P., & Nair, S. (2022). \"Artificial Intelligence in Assistive Technologies for the Hearing Impaired.\"Journal of Human-Computer Interaction, 39(5), 237-255. [9] Kumar, H., & Sen, D. (2021). \"Deep Learning-Based Sign Language Recognition System Using Video Processing.\"International Journal of Image and Graphics, 21(4), 145-160. [10] Bose, A., & Banerjee, P. (2020). \"A Hybrid Model for Speech-to-Sign Language Conversion Using AI and Linguistics.\"IEEE Access, 8, 97825-97837.

Copyright

Copyright © 2025 Mr. Akarsh Singh, Aluru Siva Sahithi , Kakarla Manoj Kumar, Shreyank Shankar Sarwade. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.