SignboardText

Scene text detection and recognition have attracted much attention in recent years because of their potential applications. Detecting and recognizing texts in images may suffer from scene complexity and text variations. Some of these problematic cases are included in popular benchmark datasets, but only to a limited extent. In this work, we investigate the problem of scene text detection and recognition in a domain with extreme challenges. We focus on in-the-wild signboard images in which text commonly appears in different fonts, sizes, artistic styles, or languages with cluttered backgrounds. We contribute an in-the-wild signboard dataset with 79K text instances on both line-level and word-level across 2,104 scene images.

场景文本检测与识别因其潜在的应用价值，近年来受到了广泛关注。图像中的文本检测与识别任务常受制于复杂的场景环境与多样的文本形态。现有主流基准数据集虽涵盖了部分此类疑难场景，但覆盖范围十分有限。本研究针对极具挑战的场景文本检测与识别问题展开研究。我们聚焦于自然场景（in-the-wild）下的招牌图像，此类图像中的文本常呈现各异的字体、尺寸、艺术风格，或包含多语言文本，且背景杂乱无章。本次研究贡献了一个自然场景招牌数据集，该数据集包含2104张场景图像，涵盖7.9万个行级与词级文本实例。