VisionAID: AI-Powered Image Caption Generator for Visually Impaired

Vision- AID is an innovative AI- powered image captioning system developed to empower visually bloodied individualities by transubstantiating visual data into meaningful audio descriptions. using the power of deep literacy, the system uses apre-trained InceptionV3 convolutional neural network to prize high- position visual features from input images. These features are also fed into an LSTM- grounded decoder model trained on the Flickr8k dataset to induce grammatically coherent and contextually accurate captions. To insure availability, the generated captions are vocalized using the pyttsx3 textbook- to- speech machine, allowing druggies to hear descriptions of images in real time. The result runs efficiently on original systems and is erected using Python with TensorFlow and Keras for the modeling factors. VisionAID islands the gap between vision and understanding, making digital content and surroundings more accessible and inclusive for the visually disabled community.

Publication Details

Journal
PhilPapers (PhilPapers Foundation)
Published
2025-01-01
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

VisionAID: AI-Powered Image Caption Generator for Visually Impaired

PhilPapers (PhilPapers Foundation)
Multimodal Machine Learning Applications
article

VisionAID: AI-Powered Image Caption Generator for Visually Impaired

article en

Abstract

Vision- AID is an innovative AI- powered image captioning system developed to empower visually bloodied individualities by transubstantiating visual data into meaningful audio descriptions. using the power of deep literacy, the system uses apre-trained InceptionV3 convolutional neural network to prize high- position visual features from input images. These features are also fed into an LSTM- grounded decoder model trained on the Flickr8k dataset to induce grammatically coherent and contextually accurate captions. To insure availability, the generated captions are vocalized using the pyttsx3 textbook- to- speech machine, allowing druggies to hear descriptions of images in real time. The result runs efficiently on original systems and is erected using Python with TensorFlow and Keras for the modeling factors. VisionAID islands the gap between vision and understanding, making digital content and surroundings more accessible and inclusive for the visually disabled community.

PhilPapers (PhilPapers Foundation)
Quality Education
Openalex Percentile: Top 47%
Multimodal Machine Learning Applications
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