2022/07/11 by Jeonghun Baek, Yusuke Matsui, Baek, Jeonghun +3 · 2 citations
Arts and Humanities · Computer Science · #Comics and Graphic Narratives #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Video Analysis and Summarization #cs.CV
paper · pdf · doi:10.48550/arxiv.2207.04675
Accepted at ECCV 2022. 25 pages, 16 figures
arxiv created 2022/07/11 · openalex publication_date 2022/07/11 · arxiv updated 2022/07/12 · openalex created_date 2022/07/13 · openalex updated_date 2026/07/28
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.