mlsanityData to 7 Oct 2026

COO: Comic Onomatopoeia Dataset for Recognizing Arbitrary or Truncated Texts

arXiv 2207.04675 ↗First posted 11 Jul 2022cs.CVcited by 12

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Abstract (arXiv)

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 urlhttps://github.com/ku21fan/COO-Comic-Onomatopoeia.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 41 of its 61 references that are in the map. First posted on arXiv 11 Jul 2022.

Builds on

41 of 61 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 7; 2025: 2; 2026: 2.

12 papers.