Image Caption Generation for the Visually Impaired Using Deep Learning

Abstract

Providing written descriptions of visual content, image caption generation has become a vital assistive technique for people with vision impairments. In this study, an improved deep learning framework is presented to produce precise and contextually rich image captions intended for assistive technology applications. Our suggested architecture, which we refer to as ViT-BiLSTM-Attention (VBLA), combines a Vision Transformer (ViT) encoder with a bidirectional LSTM decoder enhanced by an attention mechanism. We tested our model on a novel dataset that has been specially selected for assistive technology applications, as well as on common datasets like Flickr30k and MS COCO. With a BLEU-4 score of 0.382, METEOR score of 0.417, and CIDER score of 1.142, the experimental results show that our method outperforms current approaches and achieves state-of-the-art performance. Perform a thorough user research with visually challenged volunteers to assess our approach’s practical efficacy. In this work, special difficulties of developing image captioning systems for assistive technology are addressed. These difficulties include the need for detailed spatial descriptions, the recognition of important objects, and natural language generation that gives users with visual impairments priority over irrelevant information.

Introduction

Visual content is a major accessibility challenge for 285 million people with visual impairments. While screen readers help with text, images often lack adequate descriptions. Automated image captioning can provide useful audio feedback, but captions need to focus on spatial, navigational, and safety information relevant to visually impaired users rather than just factual accuracy.

This study introduces VBLA, a novel deep learning model combining Vision Transformers (ViT) for image feature extraction, Bidirectional LSTMs for better context understanding, and a specialized spatial-semantic attention mechanism that highlights navigation- and safety-critical elements in images. The architecture is tailored to assistive technology needs, focusing on relevant and context-aware captions.

Key contributions include:

• An end-to-end model prioritizing visually impaired users' information needs.

• A new attention mechanism emphasizing spatially important and navigational features.

• Evaluation on standard datasets (Flickr30k, MS COCO) and a newly created Visual Assistance Dataset (VAD) focused on assistive scenarios.

• A user study validating the model’s practical benefits for visually impaired individuals.

Results show that VBLA outperforms previous state-of-the-art models on both general and assistive captioning metrics, especially improving semantic relevance and navigational clarity. User feedback confirms its superior informativeness, safety awareness, and real-world usefulness.

Challenges include computational intensity, domain adaptation, need for caption customization, and current inference speed limitations for real-time use.

Conclusion

VBLA, a revolutionary deep learning architecture for picture caption generation created especially for assistive technology applications, was presented in this study. Our method provides captions that better satisfy the demands of visually impaired users while achieving state-of-the-art performance on common benchmarks by combining Vision Transformers, Bidirectional LSTMs, and a specific attention mechanism. Our thorough assessment, which includes user trials with visually impaired participants, specialist assistive relevance grading, and conven- tional metrics, shows how effective our strategy is. Future work will focus on several key directions: 1) Personalization: Developing methods to customize gen- erated captions based on individual user preferences and specific visual impairments 2) Multimodal integration: Combining image caption gen- eration with other sensory information (e.g., audio) for more comprehensive scene understanding 3) Efficiency optimization: Improving inference speed and reducing computational requirements to enable real-time applications on mobile devices user population 4) Multilingual support: Extending the model to generate captions in multiple languages to serve a more diverse 5) Expanded datasets: Creating larger and more diverse datasets specifically for assistive technology applications One significant step toward more inclusive technology is the development of image caption creation for visually challenged users. We can create solutions that greatly improve the access to information and the quality of life of this crucial user group by further developing these strategies and addressing the particular requirements of visually impaired people.

References

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Copyright

Copyright © 2025 Shruti Deshpande, Ritul Adhav, Shaikh Noorbano Mohammad Kayyum. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.