A Comparative Study of Continuous Sign Language Recognition Techniques

TL;DR

This paper empirically evaluates recent deep learning methods for continuous sign language recognition across multiple datasets, establishing new benchmarks and analyzing their robustness and generalization capabilities.

Contribution

It provides a comprehensive comparison of CSLR techniques on diverse datasets, highlighting their strengths and limitations in real-world scenarios.

Findings

Models achieved new state-of-the-art results on benchmark datasets.

Performance varied significantly across different sign languages and signer variations.

The study offers insights into the robustness of CSLR models under challenging conditions.

Abstract

Continuous Sign Language Recognition (CSLR) focuses on the interpretation of a sequence of sign language gestures performed continually without pauses. In this study, we conduct an empirical evaluation of recent deep learning CSLR techniques and assess their performance across various datasets and sign languages. The models selected for analysis implement a range of approaches for extracting meaningful features and employ distinct training strategies. To determine their efficacy in modeling different sign languages, these models were evaluated using multiple datasets, specifically RWTH-PHOENIX-Weather-2014, ArabSign, and GrSL, each representing a unique sign language. The performance of the models was further tested with unseen signers and sentences. The conducted experiments establish new benchmarks on the selected datasets and provide valuable insights into the robustness and generalization of the evaluated techniques under challenging scenarios.