Enhancing ASL Recognition with GCNs and Successive Residual Connections

TL;DR

This paper introduces a novel GCN-based approach with residual connections for improved ASL recognition, achieving state-of-the-art accuracy by leveraging MediaPipe landmarks and robust preprocessing.

Contribution

It combines GCNs with residual connections and landmark-based graph representations to significantly enhance ASL recognition accuracy.

Findings

Validation accuracy of 99.14%

Superior generalization over previous methods

Effective landmark-based graph modeling

Abstract

This study presents a novel approach for enhancing American Sign Language (ASL) recognition using Graph Convolutional Networks (GCNs) integrated with successive residual connections. The method leverages the MediaPipe framework to extract key landmarks from each hand gesture, which are then used to construct graph representations. A robust preprocessing pipeline, including translational and scale normalization techniques, ensures consistency across the dataset. The constructed graphs are fed into a GCN-based neural architecture with residual connections to improve network stability. The architecture achieves state-of-the-art results, demonstrating superior generalization capabilities with a validation accuracy of 99.14%.