Abstract
ABSTRACT Visually impaired individuals actively utilize devices like computers, tablets, and smartphones, due to advancements in screen reader technologies. Integrating freely available deep learning models, image captioning can further enhance these readers, providing an affordable assistive tech solution. This research outlines the critical software requirements necessary for image captioning tools to effectively serve this demographic. Two qualitative investigations were conducted to determine these requirements. An online survey was first conducted to identify the main preferences of visually impaired users in relation to audio descriptive software, with findings visualized using word clouds. A subsequent study evaluated the proficiency of existing deep learning captioning models in addressing these stipulated requirements. Emphasizing the need for comprehensive image data, the results highlighted three primary areas: 1) characteristics of individuals, 2) color specifics of objects, and 3) the overall context of images. The research indicates that current captioning tools are not entirely effective for the visually impaired. Based on the delineated requirements and suggested future research paths, there is potential for the development of improved image captioning systems, advancing digital accessibility for the visually impaired.
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