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Development of a Fully Autonomous Offline Assistive System for Visually Impaired Individuals: A Privacy-First

Fitsum Yebeka Mekonnen1,2, Mohammad F Al Bataineh3, Dana Abu Abdoun3

  • 1Department of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates.

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Summary

This study introduces an offline AI system for visually impaired individuals, using open-source tools on a Raspberry Pi 5 for privacy and accessibility. The system provides real-time object detection, recognition, and voice control without cloud dependence.

Keywords:
OCRRaspberry Piassistive technologyface recognitionobject detectionoffline AIvoice recognition

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Area of Science:

  • Assistive Technology
  • Artificial Intelligence
  • Computer Vision

Background:

  • Visual impairment presents significant challenges to independence and environmental interaction.
  • Current assistive technologies often rely on cloud processing, posing privacy risks and accessibility issues in low-resource settings.

Purpose of the Study:

  • To develop and evaluate a fully offline, privacy-preserving assistive system for visually impaired individuals.
  • To explore the integration of open-source AI models on low-power edge hardware for assistive applications.

Main Methods:

  • Integration of YOLOv8 for object detection, Tesseract for OCR, and VOSK for offline voice command control on a Raspberry Pi 5.
  • Implementation of face recognition with voice-guided registration and Piper for audio feedback.
  • Development of a hands-free, multimodal interaction system independent of cloud infrastructure.

Main Results:

  • The system achieved 75-90% detection and recognition accuracies in controlled evaluations.
  • Demonstrated sub-second response times, ensuring real-time environmental awareness.
  • Confirmed the feasibility of effective assistive functionality using open-source tools on edge hardware.

Conclusions:

  • Open-source AI models can power effective, offline assistive systems for the visually impaired.
  • The developed platform prioritizes user privacy, low latency, and affordability.
  • This approach is suitable for privacy-sensitive and resource-constrained environments.