  Comic strips are a popular and expressive form of visual storytelling that
can convey humor, emotion, and information. However, they are inaccessible to
the BLV (Blind or Low Vision) community, who cannot perceive the images,
layouts, and text of comics. Our goal in this paper is to create natural
language descriptions of comic strips that are accessible to the visually
impaired community. Our method consists of two steps: first, we use computer
vision techniques to extract information about the panels, characters, and text
of the comic images; second, we use this information as additional context to
prompt a multimodal large language model (MLLM) to produce the descriptions. We
test our method on a collection of comics that have been annotated by human
experts and measure its performance using both quantitative and qualitative
metrics. The outcomes of our experiments are encouraging and promising.
