Real-Time Sign Language Translator: Bridging Communication Barriers with Mobile Machine Learning

Sign language is the primary form of communication in the deaf and hard-of-hearing community; however, this is not often understood by the general population. This paper details a real-time Sign Language Translator developed using Flutter and TensorFlow Lite. The system captures hand gestures with a smartphone camera and then processes them with a custom-trained machine learning model to translate real time gesture input to textual output. Our approach emphasizes accessibility, taking benefit of mobile technology to create a portable solution. The capability of the system was tested on different devices with an accuracy of 85% in controlled environments. Future work will be focusing on increasing the complexity of gestures, multilingual support & wearable technology integration.

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Publication Details

Published
2025-03-06
DOI
https://doi.org/10.1109/ispcc66872.2025.11039399
Primary Topic
Robotics and Automated Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Real-Time Sign Language Translator: Bridging Communication Barriers with Mobile Machine Learning

Mitu Sehgal, Shally Nagpal, Santosh K. Gupta, Rashmi Makkar
Robotics and Automated Systems
article

Real-Time Sign Language Translator: Bridging Communication Barriers with Mobile Machine Learning

Mitu Sehgal, Shally Nagpal, Santosh K. Gupta, Rashmi Makkar
article en

Abstract

Sign language is the primary form of communication in the deaf and hard-of-hearing community; however, this is not often understood by the general population. This paper details a real-time Sign Language Translator developed using Flutter and TensorFlow Lite. The system captures hand gestures with a smartphone camera and then processes them with a custom-trained machine learning model to translate real time gesture input to textual output. Our approach emphasizes accessibility, taking benefit of mobile technology to create a portable solution. The capability of the system was tested on different devices with an accuracy of 85% in controlled environments. Future work will be focusing on increasing the complexity of gestures, multilingual support & wearable technology integration.

Openalex Percentile: Top 84%
Robotics and Automated Systems
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Real-Time Sign Language Translator: Bridging Communication Barriers with Mobile Machine Learning — Mitu Sehgal, Shally Nagpal, et al. · (2025) | TGRS Research Map | TGRS