Systematic Literature Review on Computational Models Used For Sign Language Recognition

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ID: 312657
2026
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Abstract
Sign Language Recognition (SLR) is a popular research area, but it’s not much focused due to its complex nature and resource limitation. In this review, a unique method for developing a SLR have been studied in which an automatic sign-language recognition system has been proposed. A comprehensive review of different studies and working models from 2015 to 2025. Total 60 different studies with different methodology are reviewed in this systematic literature review. It has been found that American Sign Language (ASL) is one of the most commonly used data set for various studies. MediaPipe Holistic model, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are some of the techniques which are most focused in various studies. Our work is unique, we have presented a comprehensive taxonomy of approaches and we established timeline of approaches that have been focused in literature guiding us to suggest which approach can be followed in future. We have also identified the most focused dataset, mostly processed in literature and region focused. As valuable contribution in SLR, our systematic literature review presents state of the art review exploring multiple dimensions of SLR field and would serve research.
Reference Key
imported_1777055839_69ebb85f3068d Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mohsin Sami, Rabia Tehseen, Uzma Omer, Muhammad Farrukh Khan, Shahan Yamin Siddiqui, Nabeel Sabir Khan, Danish Ali Khan
Journal Journal of Computing & Biomedical Informatics
Year 2026
DOI
10.56979/1002/2026/1140
URL
https://jcbi.org/index.php/Main/article/view/1140
Keywords Keywords not found

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