SignboardText: Text Detection and Recognition in In-the-Wild Signboard Images

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 first contribute an in-the-wild signboard dataset with 79K text instances on both line-level and word-level across 2,104 scene images. We then comprehensively evaluated recent state-of-the-art (SOTA) approaches for text detection and recognition on the dataset. By doing this, we expect to realize the barriers of current state-of-the-art approaches to solving the extremely challenging issues of scene text detection and recognition, as well as their applicability in this domain. Code and dataset are available at https://github.com/aiclub-uit/SignboardText/ and IEEE DataPort.