AuslanWeb: A Scalable Web-Based Australian Sign Language Communication System for Deaf and Hearing Individuals

Effective communication between the deaf community and hearing individuals facilitates social inclusion, equal opportunities, and the dignity of vulnerable populations. However, existing region-specific sign language systems are constrained by limited training datasets and narrow topic domains, rendering them ineffective for bridging the linguistic gaps between sign languages and spoken languages. Auslan, as the sign language specific to Australia, still lacks a reliable bidirectional translation tool for effective communication. To address these challenges, we propose AuslanWeb, a web-based system for bidirectional translation of both isolated and successive sign language. For the former, AuslanWeb achieves high-precision mapping between isolated signs (glosses) and spoken language words or phrases through a multimodal recognition system and a versatile Auslan dictionary. For the latter, it leverages the advanced contextual understanding and text generation capabilities of Large Language Models (LLMs) to support bidirectional translation between successive sign language videos and long-form spoken language. By integrating linguistic structure with advanced AI capabilities, AuslanWeb overcomes the limitations of dataset dependency and enhances the scalability of sign language translation systems. The effectiveness of the system is further validated through user feedback, receiving consistent praise from Auslan experts, Australian deaf individuals, and volunteers. The demo video of AuslanWeb is provided here.

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Publication Details

Published
2025-04-22
DOI
https://doi.org/10.1145/3696410.3714525
Primary Topic
Hand Gesture Recognition Systems
Type
article
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article

AuslanWeb: A Scalable Web-Based Australian Sign Language Communication System for Deaf and Hearing Individuals

Hongwei Sheng, Heming Du, Lincheng Li, Xiaoli Shen et al.
Hand Gesture Recognition Systems
article

AuslanWeb: A Scalable Web-Based Australian Sign Language Communication System for Deaf and Hearing Individuals

Hongwei Sheng, Heming Du, Lincheng Li, Xiaoli Shen, Kaihao Zhang
article en

Abstract

Effective communication between the deaf community and hearing individuals facilitates social inclusion, equal opportunities, and the dignity of vulnerable populations. However, existing region-specific sign language systems are constrained by limited training datasets and narrow topic domains, rendering them ineffective for bridging the linguistic gaps between sign languages and spoken languages. Auslan, as the sign language specific to Australia, still lacks a reliable bidirectional translation tool for effective communication. To address these challenges, we propose AuslanWeb, a web-based system for bidirectional translation of both isolated and successive sign language. For the former, AuslanWeb achieves high-precision mapping between isolated signs (glosses) and spoken language words or phrases through a multimodal recognition system and a versatile Auslan dictionary. For the latter, it leverages the advanced contextual understanding and text generation capabilities of Large Language Models (LLMs) to support bidirectional translation between successive sign language videos and long-form spoken language. By integrating linguistic structure with advanced AI capabilities, AuslanWeb overcomes the limitations of dataset dependency and enhances the scalability of sign language translation systems. The effectiveness of the system is further validated through user feedback, receiving consistent praise from Auslan experts, Australian deaf individuals, and volunteers. The demo video of AuslanWeb is provided here.

Australian National University (AU), The University of Queensland (AU), NetEase (China) (CN)
Quality Education
Openalex Percentile: Top 94%
Hand Gesture Recognition Systems
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