A Global Taxonomy and Computational Characterization of Major Sign Languages for Scalable AI Recognition Systems

A Global Taxonomy and Computational Characterization of Major Sign Languages for Scalable AI Recognition Systems | 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 Article A Global Taxonomy and Computational Characterization of Major Sign Languages for Scalable AI Recognition Systems M. Neela Harish, B Rashmi, M Srilakshmi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9228752/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Sign language is characterized as natural visual-gestural language with autonomous linguistic structures, varied orthographic influences, and legal status. Despite the recent advancements in deep learning-based recognition systems, these systems face the problem of regional biases, which limits the scalability of the systems and excludes millions of users from low-resource backgrounds. The studyfocuses on a comprehensive Global Taxonomy and Computational Characterization of several significant sign languages across five continents of the world. These languages are classified based on six crucial parameters: alphabetic adaptation, linguistic family, legal status, user population, manual spelling structures: one-handed or two-handed. The research proposes a Hierarchical Transformer Architecture capable of accommodating various linguistic variations. It follows athree-layer approach: a universal hand pose encoder, family-specific adaptation, classifier head. Moreover, it presents a Global Sign Language Complexity Index (GSLCI) and a Fairness Index to measure the complexities of various sign language. Analysis of the proposed optimized framework indicates a maximum accuracy of 91% using the common features of various language families which can be considered a preferable mode of communication for people with hearing and speech impairments. Humanities/Language and linguistics Social science/Language and linguistics Physical sciences/Mathematics and computing Sign Language Recognition Global Taxonomy Cross-Lingual AI Lexical Similarity Accessibility Computing Multilingual Gesture Modelling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 28 Apr, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor invited by journal 08 Apr, 2026 Editor assigned by journal 27 Mar, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 25 Mar, 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. 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-9228752","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":626331849,"identity":"bfbcbe53-6758-4b09-bed0-5213a6e5918b","order_by":0,"name":"M. 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