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May 8, 2026Humanities and Social Sciences CommunicationsOpen Access

Designing an accessible and interactive shopping assistant using deep learning for visually impaired individuals in Pakistan

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Authors

MMMariyam ManzoorTATauqir AhmadAAAyesha Altaf

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Overview

Randomized trial evaluates a deep learning shopping assistant for visually impaired, indicating improved independence.

Key Points

  • The central aim is to develop an assistive shopping system for visually impaired individuals to enhance their independence in retail environments.
  • Developed a shopping assistant system using computer vision and OCR for real-time product identification and audio feedback.
  • Created a custom dataset from videos in various retail settings to improve recognition accuracy for product details like price and expiration date.
  • Evaluated four models, with YOLOv10 achieving the highest mean average precision at 98%.
  • YOLOv10 outperformed other models with a mean average precision of 98%.
  • A preliminary usability test demonstrated practical effectiveness in enhancing independent shopping.
  • The system successfully identified products and provided auditory feedback to users.

Cite This Study

Manzoor et al. (2026) studied this question.

synapsesocial.com/papers/69fd7ef7bfa21ec5bbf074bchttps://doi.org/10.1057/s41599-025-06313-6
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1YOLOv5 driven smart glasses for visually impaired2024 · 7 citations
  2. 2Smart Shopping Facilitator for Visually Impaired using Artificial Intelligence2024 · 1 citations
  3. 3Development of a Product Purchase Assistance Application for the Visually Impaired2025
  4. 4Integrated Intelligent Assistant for Visually Impaired2024 · 3 citations
  5. 5Smart Shopping Trolley based on IoT and AI for the Visually Impaired2024