We introduce a new arbitrary-shaped text detection approach named ReLaText by
formulating text detection as a visual relationship detection problem. To
demonstrate the effectiveness of this new formulation, we start from using a
"link" relationship to address the challenging text-line grouping problem
firstly. The key idea is to decompose text detection into two subproblems,
namely detection of text primitives and prediction of link relationships
between nearby text primitive pairs. Specifically, an anchor-free region
proposal network based text detector is first used to detect text primitives of
different scales from different feature maps of a feature pyramid network, from
which a text primitive graph is constructed by linking each pair of nearby text
primitives detected from a same feature map with an edge. Then, a Graph
Convolutional Network (GCN) based link relationship prediction module is used
to prune wrongly-linked edges in the text primitive graph to generate a number
of disjoint subgraphs, each representing a detected text instance. As GCN can
effectively leverage context information to improve link prediction accuracy,
our GCN based text-line grouping approach can achieve better text detection
accuracy than previous text-line grouping methods, especially when dealing with
text instances with large inter-character or very small inter-line spacings.
Consequently, the proposed ReLaText achieves state-of-the-art performance on
five public text detection benchmarks, namely RCTW-17, MSRA-TD500, Total-Text,
CTW1500 and DAST1500.

本论文提出了一种名为 ReLaText 的新型任意形状文本检测方法，通过采用将文本检测转化为视觉关系检测问题的方法，使用基于锚点自由区域提案网络的文本检测器以及基于图卷积网络（GCN）的链接关系预测模块，实现了更为准确和高效的文本检测。实验结果表明，ReLaText 达到了 5 个公共文本检测基准（RCTW-17、MSRA-TD500、Total-Text、CTW1500 和 DAST1500）的最佳性能。