Text detection in natural scene images by hierarchical localization and growing of textual components

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.