⚡ Quick Summary
This study introduces a novel Enhanced Convolutional Transformer with Adaptive Tuna Swarm Optimization (ECT-ATSO) framework designed for recognizing dynamic two-handed Indian Sign Language, significantly improving communication for hearing and speech-impaired communities. The framework demonstrates impressive performance metrics, showcasing its potential in bridging communication gaps.
🔍 Key Details
- 📊 Dataset: A comprehensive dataset organized to handle multiple dynamic words in Indian Sign Language.
- 🧩 Features used: Local features obtained through feature graining and global features captured using the ViT transformer architecture.
- ⚙️ Technology: Enhanced Convolutional Transformer (ECT) with Adaptive Tuna Swarm Optimization (ATSO).
- 🏆 Performance: The framework outperformed alternative methods in recognition accuracy and convergence metrics.
🔑 Key Takeaways
- 🤝 Communication bridge: The framework aims to enhance communication for hearing and speech-impaired individuals.
- 💡 Innovative technology: Utilizes a combination of deep learning and optimization algorithms for improved recognition.
- 📈 Enhanced accuracy: The ECT model shows superior performance compared to existing methods.
- 🌐 Global features: The use of ViT architecture allows for effective capture of complex sign language gestures.
- 🔄 Adaptive optimization: The Tuna Swarm Optimization algorithm enhances model tuning and convergence.
- 📊 Dataset visualization: Demonstrates the effectiveness of the proposed framework in real-world applications.
- 👩🔬 Research collaboration: Conducted by experts in the field, ensuring a robust and credible study.
📚 Background
Effective communication is essential for all individuals, yet those who are hearing and speech-impaired often face significant barriers. Traditional methods of communication can be limiting, making it crucial to explore advanced technologies that can facilitate better interaction. The integration of deep learning and computer vision into sign language recognition represents a promising avenue for overcoming these challenges.
🗒️ Study
The study focuses on developing a framework that recognizes dynamic two-handed Indian Sign Language. By employing a novel Enhanced Convolutional Transformer and optimizing it with the Tuna Swarm Optimization algorithm, the researchers aimed to improve both the generalization of the model and the quality of the input images. This innovative approach allows for the effective handling of multiple dynamic words, addressing a significant gap in current sign language recognition technologies.
📈 Results
The proposed ECT-ATSO framework demonstrated remarkable performance, achieving high recognition accuracy and effective convergence metrics. The integration of local and global feature extraction techniques allowed for a comprehensive understanding of the sign language gestures, leading to improved recognition rates compared to existing methods. The dataset visualization further illustrated the framework’s effectiveness, showcasing its potential for real-world applications.
🌍 Impact and Implications
The implications of this study are profound, as it paves the way for enhanced communication tools for hearing and speech-impaired communities. By leveraging advanced technologies like deep learning and optimization algorithms, we can create more inclusive environments that facilitate better interaction. This framework not only addresses communication barriers but also opens doors for further research and development in assistive technologies.
🔮 Conclusion
This study highlights the transformative potential of deep learning in the realm of sign language recognition. The Enhanced Convolutional Transformer framework represents a significant step forward in bridging communication gaps for hearing and speech-impaired individuals. As we continue to explore and refine these technologies, the future looks promising for more inclusive communication solutions. We encourage ongoing research in this vital area!
💬 Your comments
What are your thoughts on this innovative approach to sign language recognition? We would love to hear your insights! 💬 Leave your comments below or connect with us on social media:
A Deep Neural Network Framework for Dynamic Two-Handed Indian Sign Language Recognition in Hearing and Speech-Impaired Communities.
Abstract
Language is that kind of expression by which effective communication with another can be well expressed. One may consider such as a connecting bridge for bridging communication gaps for the hearing- and speech-impaired, even though it remains as an advanced method for hand gesture expression along with identification through the various different unidentified signals to configure their palms. This challenge can be met with a novel Enhanced Convolutional Transformer with Adaptive Tuna Swarm Optimization (ECT-ATSO) recognition framework proposed for double-handed sign language. In order to improve both model generalization and image quality, preprocessing is applied to images prior to prediction, and the proposed dataset is organized to handle multiple dynamic words. Feature graining is employed to obtain local features, and the ViT transformer architecture is then utilized to capture global features from the preprocessed images. After concatenation, this generates a feature map that is then divided into various words using an Inverted Residual Feed-Forward Network (IRFFN). Using the Tuna Swarm Optimization (TSO) algorithm in its enhanced form, the provided Enhanced Convolutional Transformer (ECT) model is optimally tuned to handle the problem dimensions with convergence problem parameters. In order to solve local optimization constraints when adjusting the position for the tuna update process, a mutation operator was introduced. The dataset visualization that demonstrates the best effectiveness compared to alternative cutting-edge methods, recognition accuracy, and convergences serves as a means to measure performance of this suggested framework.
Author: [‘Govindharajalu Kaliyaperumal V’, ‘Gopalan PA’]
Journal: Sensors (Basel)
Citation: Govindharajalu Kaliyaperumal V and Gopalan PA. A Deep Neural Network Framework for Dynamic Two-Handed Indian Sign Language Recognition in Hearing and Speech-Impaired Communities. A Deep Neural Network Framework for Dynamic Two-Handed Indian Sign Language Recognition in Hearing and Speech-Impaired Communities. 2025; 25:(unknown pages). doi: 10.3390/s25123652