Abstract
Text embedded in natural scene images provide rich semantic information about the scene, which is of great value for content-based image applications. Due to the variety of text appearance and the complexity of scene context, however, text detection in natural images remains a challenging task. In this paper, we propose a robust text detection method that hierarchically and progressively localizes textual components at pixel, intra-character and inter-character levels. For each level, a seed growing mechanism is adopted, which starts by detecting well-conditioned seed textual components and then grows from the seeds to localize related degraded components, exploiting the cues captured by the seeds. We further propose a random walk with restart algorithm to robustly aggregate character candidates into text lines. The experiment on public scene text datasets demonstrates the state-of-the-art performance of the proposed method.