  The expanding market for e-comics has spurred interest in the development of
automated methods to analyze comics. For further understanding of comics, an
automated approach is needed to link text in comics to characters speaking the
words. Comics speaker detection research has practical applications, such as
automatic character assignment for audiobooks, automatic translation according
to characters' personalities, and inference of character relationships and
stories.
  To deal with the problem of insufficient speaker-to-text annotations, we
created a new annotation dataset Manga109Dialog based on Manga109.
Manga109Dialog is the world's largest comics speaker annotation dataset,
containing 132,692 speaker-to-text pairs. We further divided our dataset into
different levels by prediction difficulties to evaluate speaker detection
methods more appropriately. Unlike existing methods mainly based on distances,
we propose a deep learning-based method using scene graph generation models.
Due to the unique features of comics, we enhance the performance of our
proposed model by considering the frame reading order. We conducted experiments
using Manga109Dialog and other datasets. Experimental results demonstrate that
our scene-graph-based approach outperforms existing methods, achieving a
prediction accuracy of over 75%.
