Abstract
Developing effective scene text detection and recognition models hinges on extensive training data, which can be both laborious and costly to obtain, especially for low-resourced languages. Conventional methods tailored for Latin characters often falter with non-Latin scripts due to challenges like character stacking, diacritics, and variable character widths without clear word boundaries. In this paper, we introduce the first Khmer scene-text dataset, featuring 1,544 expert-annotated images, including 997 indoor and 547 outdoor scenes. This diverse dataset includes flat text, raised text, poorly illuminated text, distant and partially obscured text. Annotations provide line-level text and polygonal bounding box coordinates for each scene. The benchmark includes baseline models for scene-text detection and recognition tasks, providing a robust starting point for future research endeavors. The KhmerST dataset is publicly accessible at this https URL.
Abstract (translated)
开发有效的场景文本检测和识别模型依赖于大量的训练数据，而获取这些数据既费时又昂贵，特别是对于资源匮乏的语言来说更是如此。针对拉丁字母的传统方法在处理非拉丁脚本时往往效果不佳，因为存在字符堆叠、附加符号以及没有明确单词边界的可变字符宽度等挑战。本文介绍了首个高棉语场景文本数据集，该数据集包含1,544张专家标注的图像，其中包括997张室内场景和547张室外场景。这个多样化的数据集涵盖了平面文本、凸起文本、光照条件不佳的文本、远处以及部分遮挡的文本。标注信息提供了每个场景的行级文本及多边形边界框坐标。基准测试包括了场景文本检测与识别任务的基础模型，为未来的科研工作提供了一个坚实的基础。KhmerST数据集可以在以下链接中公开访问：[此处是提供的URL]。
URL
https://arxiv.org/abs/2410.18277
