Real-Time Image Captioning for Visually Impaired Assistance Using Fine-Tuned Inception-ResNet Transfer Learning Model
International Journal of Medical Toxicology and Forensic Medicine,
Vol. 16 (2026),
1 Dey 2026
,
Page 1-9
https://doi.org/10.22037/ijmtfm.v16.52616
Abstract
Background: Visually challenged and blind people frequently face socio-economic barriers that can make it difficult for them to live independently and contribute fully to society. Currently, studies on assistive technology have advanced, leading to the development of a visual replacement for individuals with visual impairment. Image captioning is a task at the border between computer vision (CV) and natural language processing (NLP) that addresses making a textual explanation of the image. This paper presents a Metaheuristic Optimization-Based Image Captioning for Assisting Visually Challenged People Using Deep Learning (MOIC-AVCPDL) model to deliver image captioning tasks using advanced techniques.
Methods: The proposed MOIC-AVCPDL model employs InceptionResNetV2 for effective feature extraction, yielding rich, discriminative feature representations from the input data. The Bidirectional Gated Recurrent Unit technique is used for caption prediction. Furthermore, to maximize performance, the Multi-Objective Arithmetic Optimization Algorithm is utilized for hyperparameter tuning, enabling optimal parameter selection for improved accuracy.
Results: The proposed MOIC-AVCPDL achieved BLEU-1 scores of 78.08% on Flickr8K and 77.47% on Flickr30K, indicating improved image-captioning performance.
Conclusion: The proposed method delivers effective image captioning and has the potential to assist visually impaired people.
- Image captioning, Visually challenged people, Metaheuristic optimization, Feature extraction, Image pre-processing
How to Cite
References
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