Robust seed-based stroke width transform for text detection in natural images

Abstract

Text detection in natural scene images is challenging due to the significant variations of the appearance of the text itself and its interaction with the context. The popular stroke width transform (SWT) algorithm is highly efficient but sensitive to the defects of the edges extracted from the input image when searching for the matching edge pixels for computing potential stroke width. In this paper, we propose a novel seed-based variant of SWT that enhances significantly the robustness of the original algorithm to complicated image contextual interference and varied text appearance. We first search for the seed segment of strokes, which is defined as a consecutive sequence of neighbouring rays (pairs of edge pixels) with regular length and satisfying certain constraints, and grow from them to localize more stroke segments. We then exploit the principal width and direction information of stroke captured by the stroke segments detected to rectify inaccurate stroke width and recover missed stroke parts, which are resulted from erroneous and noisy edges in complex natural images. The stroke segments detected are also exploited to improve the accuracy of candidate character localization. The experimental results on public datasets demonstrated the effectiveness of the proposed method.