The core message of this paper is that by fusing visual and relational features of text segments, and using a novel shape approximation strategy, bottom-up methods can outperform state-of-the-art top-down approaches for arbitrary-shape scene text detection.
The proposed MorphText approach effectively embeds deep morphology to regularize text segments, addressing the issues of false text segment detections and missing linkages between text segments in bottom-up arbitrary-shape scene text detection methods.