Improving visual perception through technology: a
comparative analysis of real-time visual aid systems
Dublin Core
Title
Improving visual perception through technology: a
comparative analysis of real-time visual aid systems
comparative analysis of real-time visual aid systems
Subject
Accessibility
Assistive technology
Benchmarking
Deep learning
Point of interest detection
Visually impaired
Assistive technology
Benchmarking
Deep learning
Point of interest detection
Visually impaired
Description
Visually impaired individuals continue to face barriers in accessing reading and
listening resources. To address these challenges, we present a comparative analysis
of cutting-edge technological solutions designed to assist people with visual
impairments by providing relevant feedback and effective support. Our
study examines various models leveraging InceptionV3 and V4 architectures,
long short-term memory (LSTM) and gated recurrent unit (GRU) decoders, and
datasets such as Microsoft Common Objects in Context (MSCOCO) 2017. Additionally,
we explore the integration of optical character recognition (OCR),
translation tools, and image detection techniques, including scale-invariant feature
transform (SIFT), speeded-up robust features (SURF), oriented FAST and
rotated BRIEF (ORB), and binary robust invariant scalable keypoints (BRISK).
Through this analysis, we highlight the advancements and potential of assistive
technologies. To assess these solutions, we have implemented a rigorous benchmarking
framework evaluating accuracy, usability, response time, robustness,
and generalizability. Furthermore, we investigate mobile integration strategies
for real-time practical applications. As part of this effort, we have developed a
mobile application incorporating features such as automatic captioning, OCRbased
text recognition, translation, and text-to-audio conversion, enhancing the
daily experiences of visually impaired users. Our research focuses on system
efficiency, user accessibility, and potential improvements, paving the way for
future innovations in assistive technology.
listening resources. To address these challenges, we present a comparative analysis
of cutting-edge technological solutions designed to assist people with visual
impairments by providing relevant feedback and effective support. Our
study examines various models leveraging InceptionV3 and V4 architectures,
long short-term memory (LSTM) and gated recurrent unit (GRU) decoders, and
datasets such as Microsoft Common Objects in Context (MSCOCO) 2017. Additionally,
we explore the integration of optical character recognition (OCR),
translation tools, and image detection techniques, including scale-invariant feature
transform (SIFT), speeded-up robust features (SURF), oriented FAST and
rotated BRIEF (ORB), and binary robust invariant scalable keypoints (BRISK).
Through this analysis, we highlight the advancements and potential of assistive
technologies. To assess these solutions, we have implemented a rigorous benchmarking
framework evaluating accuracy, usability, response time, robustness,
and generalizability. Furthermore, we investigate mobile integration strategies
for real-time practical applications. As part of this effort, we have developed a
mobile application incorporating features such as automatic captioning, OCRbased
text recognition, translation, and text-to-audio conversion, enhancing the
daily experiences of visually impaired users. Our research focuses on system
efficiency, user accessibility, and potential improvements, paving the way for
future innovations in assistive technology.
Creator
Othmane Sebban1, Ahmed Azough2, Mohamed Lamrini1
Source
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Jan 23, 2025
Contributor
PERI IRAWAN
Format
PDF
Language
ENGLISH
Type
TEXT
Files
Collection
Citation
Othmane Sebban1, Ahmed Azough2, Mohamed Lamrini1, “Improving visual perception through technology: a
comparative analysis of real-time visual aid systems,” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/10002.
comparative analysis of real-time visual aid systems,” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/10002.