SignBridge AI: A Multimodal Deep Learning Framework for Real Time Sign Language Translation

Abstract

Sign language recognition systems play an important role in improving communication between deaf or hard-of-hearing individuals and the hearing population. However, the lack of widespread understanding of sign language continues to create barriers in education, healthcare, and everyday social interaction. This paper presents a real-time vision-based sign language recognition system using Convolutional Neural Networks (CNNs) and computer vision techniques to translate hand gestures into text and speech. The proposed system captures gesture images through a webcam and processes them using OpenCV-based hand segmentation and preprocessing methods. A custom dataset consisting of 44 gesture classes, including alphabets and numerical signs, was created using grayscale images of size 50×50 pixels. Data augmentation techniques such as image flipping were applied to improve model performance and generalization. The processed gesture images were used to train a CNN model implemented using TensorFlow and Keras for accurate classification of hand gestures. The trained model enables real-time gesture prediction through live video input and converts recognized gestures into readable text and speech output using a text-to-speech module. Experimental results demonstrate that the proposed system provides efficient and accurate gesture recognition suitable for real-time applications. This work contributes to the development of an accessible, low-cost assistive communication system for sign language users.

Introduction

The study highlights the critical role of sign language recognition and translation in bridging the communication gap between deaf or hard-of-hearing individuals and the hearing population. Traditional methods, such as sensor-based gloves and handcrafted feature extraction, were limited by cost, intrusiveness, and low scalability. Advances in AI, computer vision, and deep learning—particularly CNNs, RNNs, attention mechanisms, and transformer-based models—have enabled automated, vision-based systems capable of real-time gesture recognition and translation. Multimodal approaches integrating skeletal keypoints, facial expressions, and context-aware frameworks further enhance accuracy and semantic understanding.

Despite progress, challenges remain, including dataset scarcity, gesture variability, environmental sensitivity, computational complexity, lack of grammar-aware translation, and personalization. The proposed SignBridge AI system addresses these gaps by combining 3D CNNs, Graph Convolutional Networks, Bi-LSTM, attention mechanisms, and transformer-based language models, along with speech output and grammar correction, enabling real-time, robust, and inclusive sign language translation. Future directions include multilingual, culturally diverse datasets, improved real-time deployment, and enhanced emotional and contextual understanding to expand accessibility and social inclusion for deaf communities.

Conclusion

In this paper, an intelligent and real-time sign language translation framework, SignBridge AI, has been presented to address the communication gap between deaf and hearing individuals. The proposed system integrates advanced technologies such as computer vision, deep learning, and natural language processing to enable accurate and context-aware translation of sign language into meaningful text and speech. By combining multimodal inputs, including RGB video and skeletal keypoints, with hybrid architectures such as 3D Convolutional Neural Networks, Graph Convolutional Networks, Bidirectional LSTM, attention mechanisms, and transformer-based language models, the system achieves improved spatial, temporal, and contextual understanding of gestures. Compared to traditional approaches, the proposed framework offers enhanced robustness, real-time performance, and scalability across different environments and users. It also addresses key limitations in existing systems, including gesture ambiguity, environmental sensitivity, and lack of grammatical refinement. The integration of speech output, multilingual support, and adaptive learning further improves usability and accessibility in real-world scenarios such as education, healthcare, and public services. Although challenges such as dataset availability, computational complexity, and emotional expression recognition remain, continuous advancements in artificial intelligence are expected to overcome these limitations. Overall, SignBridge AI represents a significant step toward inclusive, accessible, and intelligent communication, contributing to a more connected and equitable digital society.

References

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Copyright

Copyright © 2026 Prof. Rajeshri U. Vaidya, Sahil S. Hiwale, Shraddha V. Makode, Anurag L. Gahankari, Shravani P. Kadu, Gauri R. Watmode . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.