论文标题
COO：漫画拟合数据集用于识别任意文本或截断的文本
COO: Comic Onomatopoeia Dataset for Recognizing Arbitrary or Truncated Texts
论文作者
论文摘要
在文本识别中识别不规则的文本一直是一个具有挑战性的话题。为了鼓励对此主题进行研究，我们提供了一种新颖的漫画拟声词数据集（COO），该数据集由日本漫画中的拟声词文本组成。 COO有许多任意文本，例如极度弯曲的，部分缩小的文本或任意放置的文本。此外，有些文本分为几个部分。每个部分都是截短的文本，本身并不有意义。这些部分应链接为表示预期的含义。因此，我们提出了一个新的任务，可以预测截短的文本之间的联系。我们执行三个任务以检测拟声区域并捕获其预期的含义：文本检测，文本识别和链接预测。通过广泛的实验，我们分析了COO的特征。我们的数据和代码可在\ url {https://github.com/ku21fan/coo-comic-ononatopoeia}获得。
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}.
