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Abstract

 
 
Assistive technology for visually impaired patients has gained significant attention in recent years due to the advancements in artificial intelligence (AI) and deep learning techniques. The visually impaired community often faces numerous challenges in independent navigation, object identification, and environmental understanding. Existing models in this domain suffer from limitations such as poor accuracy, delayed response, and lack of real-time adaptability in dynamic environments. To overcome these drawbacks, this article proposes an intelligent deep learning framework designed specifically to assist visually impaired patients. The framework integrates Convolutional Neural Networks for accurate object detection, Long Short-Term Memory networks for temporal scene understanding, and a Text-to-Speech (TTS) module for real-time audio feedback. The experimental evaluation demonstrates that the proposed framework outperforms existing models in terms of accuracy, detection speed, and real-time adaptability. The results confirm the framework’s ability to provide a smart, efficient, and responsive solution for enhancing the mobility and safety of visually impaired patients in diverse environments.
Assistive technology for visually impaired patients has gained significant attention in recent years due to the advancements in artificial intelligence (AI) and deep learning techniques. The visually impaired community often faces numerous challenges in independent navigation, object identification, and environmental understanding. Existing models in this domain suffer from limitations such as poor accuracy, delayed response, and lack of real-time adaptability in dynamic environments. To overcome these drawbacks, this article proposes an intelligent deep learning framework designed specifically to assist visually impaired patients. The framework integrates Convolutional Neural Networks for accurate object detection, Long Short-Term Memory networks for temporal scene understanding, and a Text-to-Speech (TTS) module for real-time audio feedback. The experimental evaluation demonstrates that the proposed framework outperforms existing models in terms of accuracy, detection speed, and real-time adaptability. The results confirm the framework’s ability to provide a smart, efficient, and responsive solution for enhancing the mobility and safety of visually impaired patients in diverse environments.