Inspirational Modulators - Real time translation of sign language on VC tools

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Team Name	*Inspirational Modulators*
Track	*Enabling Professional Equality*
Use Case	*Real time translation of sign language on VC tools - Resource equity at Workplace*
Technology	*AI*
Region	*ASEAN*
Team lead	* Shubhabrata Bakshi*
Team members	*Aditya Malu, Sudipta Mallick, Arpan Hazra, Dipak Mahajan, Puja Sharma*
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Is your team interested in participating in an interview?	*Y*
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Real time translation of sign language on VC tools - Resource equity at Workplace

Deaf and dumb individuals often face unique challenges in professional settings due to their communication impairments. These challenges can lead to feelings of isolation, frustration, and discrimination.

Pains experienced by deaf and dumb individuals in their professional lives:

Key Factor - Communication Barriers

• Misunderstandings: Difficulty in understanding spoken language and conveying thoughts clearly can lead to misunderstandings and miscommunications.
• Isolation: Feeling isolated and excluded due to communication challenges can negatively impact job satisfaction and relationships with colleagues.
• Limited Opportunities: Communication barriers can limit career advancement opportunities, as some professions may require extensive verbal communication.

Resolution Use Case : - 

Team Inspirational Modulators proposes- AI and IOT along with Computer vision come to rescue of differently abled professional - Need of the hour to integrate such champion resources across teams and professions.  

Sign language transcription on video conferences (VCs) presents a unique challenge due to the visual nature of the language, the diversity of sign languages, and the real-time nature of VCs. IoT and AI technologies offer promising solutions to overcome these hurdles.

AI Solutions

• Computer Vision:
• Object Detection: Identify hands, fingers, and facial expressions.
• Pose Estimation: Determine the position and orientation of the signer's body parts.
• Action Recognition: Recognize specific sign language gestures and sequences.
• Natural Language Processing (NLP):
• Language Modeling: Learn the grammatical rules and semantics of sign languages.
• Machine Translation: Translate sign language into text or spoken language.
• Deep Learning:
• Convolutional Neural Networks (CNNs): Process image data to recognize visual patterns in sign language.
• Recurrent Neural Networks (RNNs): Process sequential data to capture the temporal dynamics of sign language.
• Data Processing: AI algorithms process this data to extract relevant features and recognize sign language gestures.
• Transcription: The recognized gestures are translated into text or spoken language using NLP techniques.
• Real-time Output: The transcription is displayed or spoken in real-time during the VC.

Challenges and Future Directions

• Data Scarcity: Lack of large-scale annotated datasets for various sign languages can hinder model training. However, in wake of recent Google initiative, we have a ready large data set available from https://www.kaggle.com/competitions/asl-fingerspelling
• Real-time Performance: Ensuring real-time transcription accuracy while handling variations in sign language styles, lighting conditions, and camera angles is a challenge.

Conclusion

AI technologies offer significant potential for improving sign language transcription on VCs. By harnessing the capabilities of advanced AI algorithms, we can create more inclusive and accessible communication environments for deaf and hard-of-hearing individuals. Future research and development will be crucial to address the challenges and further enhance the performance of these systems.

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