Dynamic Doubled-handed sign language Recognition for deaf and dumb people using Vision Transformers

Dynamic Doubled-handed sign language Recognition for deaf and dumb people using Vision Transformers | 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 Dynamic Doubled-handed sign language Recognition for deaf and dumb people using Vision Transformers G. K. Vaidhya, G. Paavai Anand This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3878583/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sign language is an effective communication tool to convey information to each other, that is a bridge to reduce the communication gap between deaf and dumb people. The word level sign language recognition is a challenging task due to the wide range of body gestures, unidentified signals and hand configuration. To overcome this issue, a novel Inverted Residual Network Convolutional Vision Transformer based Mutation Boosted Tuna Swarm Optimization (IRNCViT-MBTSO) algorithm is proposed for recognizing double-handed sign language. The proposed dataset is designed to identify different dynamic words and the predicted images are preprocessed to enhance the generalization ability of the model and improve image quality. The local features are extracted after performing feature graining and the global features are captured from the preprocessed images by implementing the ViT transformer model. These extracted features are concatenated to generate a feature map and are classified into different dynamic words using the Inverted Residual Feed Forward Network (IRFFN). The TSO algorithm tunes the parameters of the IRNCViT model that is responsible for handling high-dimensional problems and convergence issues. The Mutation operator is introduced in this optimization phase to escape local optimum issues while updating the position of tuna. The performance valuation of this proposed model is performed in terms of recognition accuracy, convergence and visual output of the dataset that showed the highest performance than other state-of-the-art methods. Double-handed sign language vision transformer convolutional neural network inverted residual network feed-forward network local features global features mutation operation and tuna swarm optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3878583","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268411413,"identity":"d46a7c13-7519-4dd1-bf58-68b16cfa39c9","order_by":0,"name":"G. K. 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