Paper Title
Image to audio conversion with caption generation for the visually impaired and challenged
Article Identifiers
Authors
Mamidyala Deepika , Mogilli Sathvika , Gurla Haripriya
Keywords
ResNet50 (pre-trained convolutional neural network), Feature extraction, High-level features (objects, colors, shapes), LSTM (Long Short-Term Memory network), Text generation, Textual description, Preprocessing (image resizing, normalization).
Abstract
Image-to-audio conversion technology is a ground breaking innovation that has transformed the lives of individuals with visual impairments. It’s remarkable ability to convert visual data into audio format has revolutionized the educational landscape, providing students with partial visual impairment an enriched learning experience that was previously unattainable. The technology has also democratized entertainment, making films, television programs, and online content accessible to individuals with visual impairments, thereby fostering a more inclusive media environment. Moreover, image-to-audio conversion technology has sparked a revolution in the world of visual arts, facilitating opportunities for individuals with partial visual impairment to explore, appreciate, and even create visual art. As a result, it has unleashed a wave of creativity and self-expression that was previously limited by inaccessible formats. While the technology still faces some challenges, such as accuracy and implementation costs, ongoing advancements in machine learning and artificial intelligence are poised to address these limitations, making the technology more precise, faster, and accessible.
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How To Cite (APA)
Mamidyala Deepika, Mogilli Sathvika, & Gurla Haripriya (April-2024). Image to audio conversion with caption generation for the visually impaired and challenged. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), g446-g451. https://ijnrd.org/papers/IJNRD2404652.pdf
Issue
Volume 9 Issue 4, April-2024
Pages : g446-g451
Other Publication Details
Paper Reg. ID: IJNRD_219135
Published Paper Id: IJNRD2404652
Downloads: 000121979
Research Area: Computer EngineeringÂ
Country: Hyderabad, Telangana, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2404652.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404652
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Licence
This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition


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