SignEase: AI-Based Real-Time Sign Language Recognition System

Abstract

Communication is a fundamental part of human interaction, yet it remains a challenge for hearing-impaired indi- vidualsduetolimitedunderstandingofsignlanguage.Thispaper presents SignEase, an AI-based real-time Indian Sign Language recognition system that converts hand gestures into text and speech. The system integrates MediaPipe for hand tracking and Vision Transformer (ViT) for gesture classification. A stability mechanism is introduced to ensure consistent predictions. The system achieves approximately 92% accuracy with real-time performanceof18–20FPS,makingitefficientandpractical for real-world applications. Furthermore, the proposed system emphasizes usability and scalability by providing a user-friendly interfacethatenablesseamlessinteractionbetweensignlanguage users and non-signers. The integration of real-time processing withhighaccuracyensuresthatcommunicationisbothfast andreliableinpracticalenvironments.Thesystemisdesigned tobecost-effectiveandeasilydeployable,makingitsuitable for applications in education, assistive technologies, and smart communication systems. Overall, SignEase contributes toward enhancing accessibility and promoting inclusive communication insociety.Thesystemisrobustagainstminorvariationsin hand positioning and environmental conditions, ensuring stable performance. It reduces dependency on human interpreters and enhances independent communication for users. The modular architectureallowseasyintegrationwithfuturetechnologies and datasets. This makes the solution adaptable for large-scale deployment in real-world scenarios.

Introduction

The text presents SignEase, an AI-based system designed to improve communication for hearing-impaired individuals by translating sign language gestures into text and speech in real time. Traditional solutions like interpreters are costly and not always available, while existing systems often lack accuracy and real-time performance.

SignEase uses advanced technologies such as computer vision and deep learning, particularly Vision Transformers (ViT) and MediaPipe, to achieve accurate and fast gesture recognition. The system is designed to work in real-world conditions, handling variations in lighting, background, and hand movements while maintaining prediction stability.

Earlier methods relied on hardware like sensor gloves, which were accurate but expensive and impractical. Modern approaches using CNNs improved performance but had limitations in capturing global context, which ViT overcomes by modeling both local and global features.

The system architecture includes multiple modules such as hand detection, image preprocessing, gesture classification, and output management. It uses a Flask-based web interface to provide real-time visual and audio feedback. Data is collected using webcams under diverse conditions to ensure robustness, and techniques like data augmentation and optimization improve model performance.

Conclusion

SignEase v2.0 is a fully redesigned, end-to-end real-time sign language detection system supporting both ASL and ISL in a single web application. Version 2.0 introduces a premium glassmorphism-based user interface, a reliable text-to-speech engine, and integration with advanced AI services such as NVIDIA NIM. The system demonstrates significant improvements in us- ability, performance, and scalability, making it suitable for real-world assistive communication applications.

References

[1] Google,“MediaPipeHands:On-deviceReal-timeHandTracking,” Available:https://developers.google.com/mediapipe [2] HuggingFace,“TransformersLibraryDocumentation,”Available:https://huggingface.co/docs/transformers [3] OpenCV,“OpenSourceComputerVisionLibrary,”Available:https://opencv.org/ [4] A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformersfor Image Recognition at Scale,” in Proc. ICLR, 2021 [5] TensorFlow, “Machine Learning Framework,” Available:https://www.tensorflow.org/ [6] PyTorch,“DeepLearning Framework,”Available: https://pytorch.org/ [7] D. Zhang et al., “Hand Gesture Recognition Based on Deep Learning:A Review,” in IEEE Access, 2020

Copyright

Copyright © 2026 Prof. Shivraj Kone, Shivam Ram Patil, Nikhil Santosh Pawase, Prathamesh Sanjay Pol, Rohan Pramod Pawar. 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.