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.

近年来，场景文本检测与识别因其潜在的广泛应用而备受关注。在图像中检测和识别文本可能受到场景复杂性和文本变异性等因素的影响。一些具有代表性的基准数据集中包含了部分此类问题案例，但范围有限。本研究针对一个极具挑战性的领域，对场景文本检测与识别问题进行了深入探讨。我们聚焦于野外场景中的路标图像，其中文本通常以不同的字体、尺寸、艺术风格或语言呈现，背景杂乱无章。我们贡献了一个包含79K个文本实例的野外路标数据集，这些实例涵盖了2,104张场景图像中的行级和词级文本。