ReLaText: Exploiting visual relationships for arbitrary-shaped scene text detection with graph convolutional networks

Highlights：

• We adopt a GCN-based visual relationship detection framework to effectively leverage context information to improve link prediction accuracy so that even text instances with large inter-character or very small inter-line spacing can be robustly detected by our text detector.

• Significantly improved text detection accuracy demonstrates the effectiveness of visual relationship prediction based text-line grouping and the effectiveness of GCN-based link relationship prediction.

• Our new text detector has achieved state-of-the-art performance on five public text detection benchmarks, namely RCTW-17, MSRA-TD500, Total-Text, CTW1500 and DAST1500.

摘要

•We propose a new arbitrary-shaped text detection approach by formulating text detection as a visual relationship detection problem and demonstrate the effectiveness of this new formulation by starting from using a “link” relationship to address the challenging text-line grouping problem.•We adopt a GCN-based visual relationship detection framework to effectively leverage context information to improve link prediction accuracy so that even text instances with large inter-character or very small inter-line spacing can be robustly detected by our text detector.•Significantly improved text detection accuracy demonstrates the effectiveness of visual relationship prediction based text-line grouping and the effectiveness of GCN-based link relationship prediction.•Our new text detector has achieved state-of-the-art performance on five public text detection benchmarks, namely RCTW-17, MSRA-TD500, Total-Text, CTW1500 and DAST1500.