An Efficient Image to Speech Generation System for Visually Impaired Individuals Using Deep Learning Techniques

As the world is becoming more visually oriented, visually impaired people are struggling to access and understand visual content. This paper aims to represent the model created to assist blind and visually impaired people. This method leverages deep learning techniques to convert images into meaningful audio outputs. It involves capturing input images and processing them using advanced deep-learning algorithms. The extracted features are translated into speech. The resulting auditory output provides an alternative sensory experience for individuals with visual impairments, enabling them to perceive and interpret visual information through sound. This study implements four deep learning models namely CNN-LSTM, Resnet34, YOLOv5 and Vision Transformer to process sequential visual data capturing intricate patterns and dependencies within images. By integrating these models in this work, the Image to audio converter not only achieves superior accuracy in image recognition but also enhances the richness and depth of the auditory output, creating a nuanced and immersive sensory experience for users. The performance of the proposed models was evaluated using several metrics and it was noted that vision transformer outperformed the remaining models. This seamless integration of deep learning models amplifies the study's effectiveness in translating diverse visual inputs into detailed and coherent auditory output, thereby enhancing accessibility and inclusivity for individuals with visual impairments.