Abstract: In traditional anchor box-based text detection methods for natural scenes, anchor boxes are prone to interference from other text instances, resulting in erroneous judgments or affecting accuracy. Moreover, text instances contain strong topological features, which are usually be ignored, resulting in poor performance in curved circular text detection tasks. To solve this problem, a novel neural network structure is proposed, which introduces the concept of graph convolutional networks by fully considering the relationship between adjacent anchor frames, and incorporating the topological characteristics of anchor frames to assist the learning of graph neural networks, improving the effectiveness of the overall network. The ablation experiments were conducted on two publicly available natural scene text detection datasets. In the CTW1500 dataset, the proposed method improved the model by approximately 3.0%, 1.9%, and 2.5% in terms of recall, accuracy, and F-score, respectively, and in the Totel-Text dataset , the three values were improved by approximately 2.2%, 1.8%, and 2.0%, respectively. In addition, the proposed method has also been compared with other text detection algorithms proposed in recent years. Experimental results show that the proposed method performs well for text detection in complex natural scenes, demonstrating the promising effectiveness of the proposed module for improving the performance of text detection.

Key words: text detection, natural scene, graph convolutional networks(GCN), topological feature