  Recognizing irregular texts has been a challenging topic in text recognition.
To encourage research on this topic, we provide a novel comic onomatopoeia
dataset (COO), which consists of onomatopoeia texts in Japanese comics. COO has
many arbitrary texts, such as extremely curved, partially shrunk texts, or
arbitrarily placed texts. Furthermore, some texts are separated into several
parts. Each part is a truncated text and is not meaningful by itself. These
parts should be linked to represent the intended meaning. Thus, we propose a
novel task that predicts the link between truncated texts. We conduct three
tasks to detect the onomatopoeia region and capture its intended meaning: text
detection, text recognition, and link prediction. Through extensive
experiments, we analyze the characteristics of the COO. Our data and code are
available at \url{https://github.com/ku21fan/COO-Comic-Onomatopoeia}.
