COO: Comic Onomatopoeia Dataset for Recognizing Arbitrary or Truncated Texts
This addresses a domain-specific challenge for researchers in text recognition, particularly for comic analysis, but is incremental as it focuses on a new dataset and task rather than a fundamental breakthrough.
The paper tackles the problem of recognizing irregular texts in Japanese comics by introducing the COO dataset, which includes arbitrary and truncated onomatopoeia, and proposes a link prediction task to connect truncated parts, achieving analysis through experiments on detection, recognition, and linking.
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}.