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Evolving Approaches to Static American Sign Language Fingerspelling Recognition
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Author(s): Ajay Menon (School of Mathematical and Computer Sciences, Heriot Watt University, Dubai, UAE)and Mahmoud A. A. Mousa (School of Mathematical and Computer Sciences, Heriot Watt University, Dubai, UAE)
Copyright: 2026
Pages: 36
Source title:
Improving Quality of Life for People with Disabilities Through Smart Technologies
Source Author(s)/Editor(s): Ikram Ur Rehman (University of West London, UK), Moustafa Nasralla (Prince Sultan University, Saudi Arabia), Drishty Sobnath (Heriot Watt University, UAE), Muazzam Ali Khan Khattak (Quaid-i-Azam University, Pakistan)and Sundus Ali (NED University of Engineering and Technology, Pakistan)
DOI: 10.4018/979-8-3373-2033-5.ch007
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Abstract
This review synthesizes research on static American Sign Language (ASL) alphabet recognition from images, comparing traditional machine learning pipelines, convolutional neural network (CNN) transfer learning, and hybrid or transformer-based models. The analysis spans 2016 to 2025 studies that detail preprocessing, model design, and quantitative results on datasets such as the ASL Alphabet and Sign Language MNIST. Classical approaches using engineered features with classifiers such as Support Vector Machines (SVMs) or Random Forests perform well in controlled settings but rely on robust segmentation and handcrafted descriptors. Transfer learning on CNN backbones, including MobileNetV2, ResNet, EfficientNet, DenseNet, and the Visual Geometry Group (VGG) models, achieves near-perfect within-dataset accuracy; pure and modified Vision Transformers (ViTs) and CNN–transformer hybrids also reach ceiling-level performance with favorable speed-to-accuracy tradeoffs. Most evaluations remain closed set and seldom report signer-independent splits, cross-dataset transfer, or deployment metrics.
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