COO: Comic Onomatopoeia Dataset for Recognizing Arbitrary or Truncated Texts
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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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Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 7; 2025: 2; 2026: 2.
12 papers.
- 2024The Manga Whisperer: Automatically Generating Transcriptions for Comicscited by 20
- 2024CoMix: A Comprehensive Benchmark for Multi-Task Comic Understandingcited by 15
- 2024Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Namescited by 10
- 2024One missing piece in Vision and Language: A Survey on Comics Understandingcited by 8
- 2024Comics Datasets Framework: Mix of Comics datasets for detection benchmarkingcited by 7
- 2024MangaUB: A Manga Understanding Benchmark for Large Multimodal Modelscited by 7
- 2025MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understandingcited by 6
- 2024Multimodal Transformer for Comics Text-Clozecited by 5
- 2025From Panels to Prose: Generating Literary Narratives from Comicscited by 5
- 2022A Comprehensive Gold Standard and Benchmark for Comics Text Detection and Recognitioncited by 4
- 2026Manga109-v2026: Revisiting Manga109 Annotations for Modern Manga Understandingcited by 1
- 2026OnomatoBridge: Onomatopoeia Translation and Rendering Pipeline in Mangacited by 0
- 2026OnomatoBridge: Onomatopoeia Translation and Rendering Pipeline in Manga18 Sep 2026
- 2026Manga109-v2026: Revisiting Manga109 Annotations for Modern Manga Understanding20 May 2026
- 2025MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding26 May 2025
- 2025From Panels to Prose: Generating Literary Narratives from Comics30 Mar 2025
- 2024One missing piece in Vision and Language: A Survey on Comics Understanding14 Sep 2024
- 2024Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names1 Aug 2024
- 2024MangaUB: A Manga Understanding Benchmark for Large Multimodal Models26 Jul 2024
- 2024CoMix: A Comprehensive Benchmark for Multi-Task Comic Understanding4 Jul 2024
- 2024Comics Datasets Framework: Mix of Comics datasets for detection benchmarking3 Jul 2024
- 2024Multimodal Transformer for Comics Text-Cloze6 Mar 2024
- 2024The Manga Whisperer: Automatically Generating Transcriptions for Comics18 Jan 2024
- 2022A Comprehensive Gold Standard and Benchmark for Comics Text Detection and Recognition27 Dec 2022