Research Project: Sign Language Translator Aurora

Research Project: Sign Language Translator Aurora | 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 Research Project: Sign Language Translator Aurora FERNANDO OSCAR BARRENECHEA ÁLVAREZ, MEDALY EULOGIO SAENZ, DIEGO ALEXANDRO PADILLA LLANCA, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6948192/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 This study presents the design and implementation of an embedded system for real-time translation of Peruvian Sign Language into both text and synthesized speech. The system employs a microcontroller equipped with a camera module to capture sign language gestures. These gestures are processed using convolutional neural networks trained with the open-source platforms TensorFlow and Keras, enabling accurate recognition and translation into words. The resulting text is then transferred to a second embedded unit, which converts the output into audible speech using a speaker module. Additionally, a secondary system is integrated using a micro- controller and a proximity sensor to enhance user interaction. When the presence of a person is detected nearby, an indicator light is activated to signal system readiness. This added feature improves usability in public or academic environments. The project aims to promote the social inclusion of individuals with hearing impairments by offering a low-cost and accessible solution to overcome communication barriers. By combining computer vision, machine learning, and embedded systems, the proposed system contributes to the development of assistive technology that can enhance autonomy, communication, and participation for people who use sign language. It is particularly suitable for use in classrooms, meetings, and other environments where inclusive communication tools are essential. Developmental Neuroscience Computer Architecture and Engineering Bioinformatics Artificial Intelligence and Machine Learning Translational Medicine Peruvian Sign Language Real-time Translation Embedded System Neural Networks Assistive Technology Hearing Impairment Full Text Additional Declarations The authors declare no competing interests. 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. 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