Edge-native static American Sign Language recognition using a lightweight geometric multilayer perceptron

Abstract

The global imperative for inclusive communication technologies necessitates the development of highly accurate, computationally efficient Sign Language Recognition (SLR) systems capable of deployment on resource-constrained edge devices. Current state-of-the-art approaches overwhelmingly rely on deep Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), which possess memory footprints and latency profiles incompatible with ultra-low-power microcontrollers. This report presents the architectural formulation, mathematical engineering, and empirical validation of an edge-native American Sign Language (ASL) recognition framework. By shifting the computational burden from brute-force pixel processing to explicit geometric feature engineering, the system utilizes the MediaPipe hand landmark regressor to extract a 21-point 3D topological skeleton. This spatial data is mathematically compressed into a dense, 30-dimensional tensor of scale-invariant geometric features, including intrinsic joint angles, normalized inter-phalangeal distances, and orthogonal projections. Classification is executed via a highly regularized, four-layer Multi-Layer Perceptron (MLP) with a memory footprint of merely 299 kilobytes. Evaluated against the Kaggle ASL Alphabet dataset, the 20-feature optimally pruned model achieved a 99.21% global F1-score outperforming the baseline model with 98.66% accuracy. The findings demonstrate a paradigm shift in TinyML assistive technology, proving that explicit mathematical sparsity outperforms high-dimensional deep learning for static spatial taxonomies.