Abstract
Current text detection datasets primarily target natural or document scenes, where text typically appear in regular font and shapes, monotonous colors, and orderly layouts. The text usually arranged along straight or curved lines. However, these characteristics differ significantly from anime scenes, where text is often diverse in style, irregularly arranged, and easily confused with complex visual elements such as symbols and decorative patterns. Text in anime scene also includes a large number of handwritten and stylized fonts. Motivated by this gap, we introduce AnimeText, a large-scale dataset containing 735K images and 4.2M annotated text blocks. It features hierarchical annotations and hard negative samples tailored for anime scenarios. %Cross-dataset evaluations using state-of-the-art methods demonstrate that models trained on AnimeText achieve superior performance in anime text detection tasks compared to existing datasets. To evaluate the robustness of AnimeText in complex anime scenes, we conducted cross-dataset benchmarking using state-of-the-art text detection methods. Experimental results demonstrate that models trained on AnimeText outperform those trained on existing datasets in anime scene text detection tasks. AnimeText on HuggingFace: this https URL
Abstract (translated)
当前的文字检测数据集主要针对自然场景或文档场景，其中文本通常以规则字体和形状、单调的颜色以及有序的布局出现。这些场景中的文字一般沿直线或曲线排列。然而，在动漫场景中，上述特征有显著差异：文字风格多样、排布不规则，并且容易与复杂的视觉元素（如符号和装饰图案）混淆。此外，动漫场景中的文本包括大量手写字体和特殊字体。 为了填补这一空白，我们引入了AnimeText，这是一个大规模的数据集，包含735K张图片和4.2M个标注的文字块。该数据集具有层次化的注释，并针对动漫场景定制了硬负样本（hard negative samples）。使用最先进的方法进行跨数据集评估表明，在动漫文字检测任务中，基于AnimeText训练的模型优于现有数据集上的模型。 为了评估AnimeText在复杂动漫场景中的鲁棒性，我们使用最先进的文本检测方法进行了跨数据集基准测试。实验结果表明，在动漫场景的文字检测任务中，基于AnimeText训练的模型比基于其他现有数据集训练的模型表现出色。可以在HuggingFace上访问AnimeText：[此链接](https://huggingface.co/datasets/this-url)（请将“this-url”替换为实际的URL）。
URL
https://arxiv.org/abs/2510.07951
