Hybrid LSTM-Transformer Architecture with Generative AI for Enhanced Indian Sign Language Recognition

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Abstract Sign language recognition plays a crucial role in bridging communication gaps between the deaf and hearing communities. Despite its importance, Indian Sign Language (ISL) remains underrepresented in technical research. This study introduces an advanced multimodal ISL recognition system leveraging a hybrid LSTM-Transformer architecture and generative AI for text-to-speech (TTS) synthesis. The model achieves over 97% accuracy across seven key gestures, addressing challenges such as dataset quality and environmental variability. By integrating generative TTS, the system enables natural, contextually relevant communication. This research contributes to the inclusivity and accessibility of ISL technology in India.
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Hybrid LSTM-Transformer Architecture with Generative AI for Enhanced Indian Sign Language Recognition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hybrid LSTM-Transformer Architecture with Generative AI for Enhanced Indian Sign Language Recognition Nidhi Goel, Lekha Rani, Pradeepta Kumar Sarangi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8827413/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Sign language recognition plays a crucial role in bridging communication gaps between the deaf and hearing communities. Despite its importance, Indian Sign Language (ISL) remains underrepresented in technical research. This study introduces an advanced multimodal ISL recognition system leveraging a hybrid LSTM-Transformer architecture and generative AI for text-to-speech (TTS) synthesis. The model achieves over 97% accuracy across seven key gestures, addressing challenges such as dataset quality and environmental variability. By integrating generative TTS, the system enables natural, contextually relevant communication. This research contributes to the inclusivity and accessibility of ISL technology in India. Hand Gestures Indian Sign Language Deep Learning Media Pipe Computer Vision Text to Speech (TTS) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviews received at journal 30 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers invited by journal 12 Mar, 2026 Editor assigned by journal 24 Feb, 2026 Submission checks completed at journal 24 Feb, 2026 First submitted to journal 24 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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