Extraction of Semantic Content and Styles in Comic Books

Digitisation of comic books would play a crucial role in identifying new areas in which digital comics can be used. Currently, existing systems in this domain lack the capacity to achieve complete digitisation. Digitisation requires a thorough analysis of the semantic content within comic books. This can be further sub-categorised as detection and identification of comic book characters, extraction and analysis of panels as well as texts, derivation of associations between characters and speech balloons, and analysis of different styles of reading. This paper provides an overview of using several object-detection models to detect semantic content in comics. This analysis showed that, under the constraint of limited computational capacity, YOLOv3 was the best-suited model out of the models evaluated. A study of text extraction and recognition using Optical Character Recognition, a method for determining associable speech balloons, as well as a distance-based approach for associations between characters and speech balloons are also presented here. This association method provides an increased accuracy compared to the Euclidean distance-based approach. Finally, a study on comic style is provided along with a learning model with an accuracy of 0.89 to analyse the reading order of comics.