Communication between hearing-impaired individuals and the general population remains a significant challenge due to the limited understanding of sign language. This paper presents a real-time sign language recognition system that utilizes computer vision and deep learning techniques to interpret hand gestures and convert them into readable text. The proposed system captures live video input through a webcam, processes the hand region using image preprocessing techniques, and classifies gestures using a Convolutional Neural Network (CNN). The system is designed to ensure high accuracy, low latency, and efficient real-time performance. A structured dataset of hand gestures is used for training, and the model is optimized to handle variations in lighting conditions, hand orientation, and background noise. The system enables continuous gesture recognition and provides immediate textual output, thereby facilitating seamless communication. The proposed solution is scalable and can be extended to support voice output and sentence formation, making it suitable for real-world assistive applications. Experimental results demonstrate the effectiveness of the system in improving accessibility and reducing communication barriers.
Keywords
Sign Language Recognition, Computer Vision, Deep Learning, CNN, Real-Time Detection, Assistive Technology, Gesture Recognition
Conclusion
The proposed real-time sign language recognition system presents an effective and scalable solution to address the communication challenges faced by hearing and speech- impaired individuals. By integrating computer vision techniques with deep learning-based classification, the system successfully interprets hand gestures and converts them into readable text, enabling seamless interaction between users and the general population.
A key contribution of this work lies in its ability to perform real-time gesture recognition with minimal latency while maintaining satisfactory accuracy. The use of Convolutional Neural Networks (CNNs) allows the system to automatically extract relevant features from input images, eliminating the need for manual feature engineering.
Additionally, preprocessing techniques such as background subtraction, normalization, and noise reduction significantly enhance detection reliability under varying conditions.
The modular architecture of the system ensures flexibility, scalability, and ease of integration with future technologies. Each component, including data acquisition, preprocessing, model classification, and output generation, operates efficiently while maintaining continuous data flow. This design approach enables the system to handle real-time input streams and ensures consistent performance across different operational scenarios.
From a practical perspective, the system offers a user- friendly interface that allows individuals to communicate using simple hand gestures without requiring specialized hardware. The reliance on a standard webcam and software- based processing makes the solution cost-effective and accessible. This enhances its potential for deployment in real-world applications such as assistive communication tools, educational platforms, and smart interactive systems.
The experimental results demonstrate that the system achieves reliable performance in recognizing gestures under controlled and moderately variable environments. The real- time feedback mechanism ensures smooth interaction, making the system suitable for everyday use. Although certain limitations exist, such as sensitivity to extreme lighting conditions and background complexity, the overall performance validates the feasibility of the proposed approach.
Furthermore, the system contributes to the broader goal of developing inclusive technologies that support accessibility and equal opportunities. By reducing dependence on human interpreters and enabling independent communication, the proposed solution empowers individuals with hearing impairments and improves their quality of life.
Future work can focus on enhancing the system by incorporating advanced deep learning models, expanding the dataset for improved generalization, and enabling dynamic gesture recognition for continuous sentence formation. Integration with speech synthesis systems can further extend functionality by converting text output into audio, creating a complete communication framework.
In conclusion, the proposed sign language recognition system demonstrates the potential of combining computer vision and artificial intelligence to solve real-world problems. It provides a strong foundation for further research and development in assistive technologies and highlights the importance of leveraging modern computational techniques to build more inclusive and intelligent systems.
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How to Cite This Paper
Ms.Ruby Angel, Akshaya GM, Nuha Zahra Fathima, Shankavi Ravichandran (2026). Sign Language Recognition System. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
