OptaliX: A Wearable AI-Based Vision Assistance System for Visually Impaired Users

OptaliX is a wearable, AI-based vision-assistance prototype designed to help visually impaired individuals access visual information such as objects, currency, printed text, products, and their surrounding environment through real-time audio feedback. Built on a Raspberry Pi Zero 2 W paired with an Arducam 8 MP IMX219 ultra-wide-angle camera, the system integrates object detection, currency recognition, product recognition, expiry-date recognition, and scene analysis into a single modular perception pipeline. Experimental testing showed approximately 91% accuracy in object detection with negligible latency, 82.69% accuracy in currency recognition (43/52) with 3–4 second latency, 100% accuracy in expiry-date recognition (55/55) with 5–6 second latency, and 92.73% accuracy in product recognition (51/55) at similar latency, while scene analysis produced correct interpretations across all evaluated inputs at approximately 7-second latency. These results demonstrate the technical feasibility of combining multiple visual-assistance functions on a single low-cost wearable platform. This work is presented as an initial feasibility study and does not claim clinical validation or readiness for large-scale deployment.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-05
DOI
https://doi.org/10.5281/zenodo.22396891
Primary Topic
Currency Recognition and Detection
Type
preprint
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preprint

OptaliX: A Wearable AI-Based Vision Assistance System for Visually Impaired Users

Aarya Sharma, Chirag Goel, Sorish Jindal
Zenodo (CERN European Organization for Nuclear Research)
Currency Recognition and Detection
preprint

OptaliX: A Wearable AI-Based Vision Assistance System for Visually Impaired Users

Aarya Sharma, Chirag Goel, Sorish Jindal
preprint en

Abstract

OptaliX is a wearable, AI-based vision-assistance prototype designed to help visually impaired individuals access visual information such as objects, currency, printed text, products, and their surrounding environment through real-time audio feedback. Built on a Raspberry Pi Zero 2 W paired with an Arducam 8 MP IMX219 ultra-wide-angle camera, the system integrates object detection, currency recognition, product recognition, expiry-date recognition, and scene analysis into a single modular perception pipeline. Experimental testing showed approximately 91% accuracy in object detection with negligible latency, 82.69% accuracy in currency recognition (43/52) with 3–4 second latency, 100% accuracy in expiry-date recognition (55/55) with 5–6 second latency, and 92.73% accuracy in product recognition (51/55) at similar latency, while scene analysis produced correct interpretations across all evaluated inputs at approximately 7-second latency. These results demonstrate the technical feasibility of combining multiple visual-assistance functions on a single low-cost wearable platform. This work is presented as an initial feasibility study and does not claim clinical validation or readiness for large-scale deployment.

Zenodo (CERN European Organization for Nuclear Research)
Public Works Department Buildings and Roads (IN)
Currency Recognition and Detection
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OptaliX: A Wearable AI-Based Vision Assistance System for Visually Impaired Users — Aarya Sharma, Chirag Goel, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS