Abstract
Comics are complex documents whose reception engages cognitive processes such as scene perception, language processing, and narrative understanding. Possibly because of their complexity, they have rarely been studied in cognitive science. Modeling the stimulus ideally requires a formal description, which can be provided by feature descriptors from computer vision and computational linguistics. With a focus on document analysis, here we review work on the computational modeling of comics. We argue that the development of modern feature descriptors based on deep learning techniques has made sufficient progress to allow the investigation of complex material such as comics for reception studies, including experimentation and computational modeling of cognitive processes.
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