International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 7 Issue 2
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Assessing Teachable Machine by deploying American Sign Language system

Author(s) Mr. Sanjeevkumar B, Mr. Varun SP, Prof. Dr. Yokesh Babu S
Country India
Abstract Sign language recognition and detection using the current technologies has become one of the focused areas of research which aims to help with communication capability and accessibility for the people of the deaf and hearing issues related community. Over the year with the improvement in the object detection space, A technology by Google ‘Teachable Machine’ which is a web-based tool that allows users to custom train machine learning models with the user’s available data, thereby acting as an automatic ML modeler for producing data models with ease. This paper proposes a method to evaluate the Teachable Machine’s effectiveness by utilizing the American Sign Language (ASL) to make the model recognize the signs once trained. The project involves data collection, image preprocessing, model training using the Teachable Machine’s and testing. The results demonstrated an overall accuracy in predicting ASL letters, though the model is struggling to predict certain letters with similar symbols. The paper also proposes the real-life applicability of such systems in various domains, strengths, and limitations of teachable machines with future scope for improvements in accuracy and real-time processing.
Keywords Sign language recognition, Object detection, Teachable Machine, Data models, Evaluate, Image preprocessing, Model training
Field Mathematics > Logic
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-08
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.40745
Short DOI https://doi.org/g9fb7p
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E-ISSN 2582-2160
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