SmartSight for the Blind Using Earphones

Abstract

This lecture examines a cutting-edge Smart Vision technology that uses headphones to provide real-time aural aid to visually impaired people, thereby increasing their independence. The system processes visual data from high-resolution cameras by utilizing cutting-edge computer vision and artificial intelligence technology. Users can confidently navigate and engage with their environment thanks to core functions including object recognition, obstacle detection, text reading via optical character recognition (OCR), and facial recognition. Multilingual audio output is ensured by the incorporation of gTTS technology, accommodating a wide range of user choices.The Smart Vision system tackles accessibility and mobility issues by fusing wearable technology with user-friendly audio interfaces, promoting inclusivity. This ground-breaking invention demonstrates how assistive technology may significantly enhance the quality of life for those who are blind or visually impaired.An innovative Smart Vision technology that redefines accessibility for those with visual impairments is shown in this session. Real-time object recognition, obstacle detection, text reading, and facial recognition are made possible by utilizing AI-powered technologies and user-friendly earphone-based aural feedback. Support in several languages guarantees inclusivity and meets the demands of a wide range of users. This invention demonstrates how assistive technology can improve confidence, freedom, and movement in daily life.

Introduction

The concept of Smart Sight for the Blind Using Earphones integrates AI, computer vision, and wearable technology to enhance mobility and independence for visually impaired individuals through real-time auditory feedback. Systems like AIris, TEXT2TASTE, DRISHTI, and smart glasses equipped with object detection use cameras, sensors, and machine learning to recognize text, obstacles, and surroundings, converting this data into spoken instructions delivered via earphones.

The methodology involves smart glasses capturing environmental data, processed by AI models (OCR for text, YOLO/CNN for objects) and natural language processing (e.g., GPT-4) to provide contextual audio feedback such as navigation cues and reading assistance. Privacy is maintained by local data processing. The system supports hands-free use and plans future features like gesture control and GPS integration.

Testing with visually impaired users showed high accuracy (over 90%) in text and object recognition, fast audio response (~1.2 seconds), and strong user satisfaction. A comparison table shows that smart glasses provide superior performance though at a higher cost compared to other assistive technologies like wearable cameras, AI assistants, GPS tools, and screen readers.

In summary, earphone-based smart sight systems leveraging AI offer a promising path to improve safety, navigation, and reading capabilities for the blind, supporting greater autonomy in daily life.

Conclusion

In conclusion, earphone-based assistive technologies for the blind and visually impaired offer notable improvements in mobility, safety, and freedom. These devices help users navigate, detect obstacles, and comprehend text by providing real-time audio feedback, which enables them to engage with their surroundings more efficiently. By providing instantaneous and easily available help, smart vision systems that use earphones have demonstrated considerable potential in enhancing the quality of life for those with visual impairments. By making navigation safer and easier, this method has the potential to completely transform everyday chores and activities for the blind. Even more responsive and customized solutions may result from future developments in earphone-based smart vision technology. Wearable technology and AI integration could increase these systems\' capabilities and provide them with more autonomy.

References

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Copyright

Copyright © 2025 Mohammed Anas P, Divya P. 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.