广东工业大学学报2024，Vol.41Issue(3)：102-109,8.DOI:10.12052/gdutxb.230011
嵌入拓扑特征的自然场景文本检测方法
Text Detection in Natural Scenes Embedded Topological Feature
摘要
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.关键词
文本检测/自然场景/图神经网络/拓扑特征Key words
text detection/natural scene/graph convolutional networks(GCN)/topological feature分类
信息技术与安全科学引用本文复制引用
郑侠聪,程良伦,黄国恒,王敬超..嵌入拓扑特征的自然场景文本检测方法[J].广东工业大学学报,2024,41(3):102-109,8.基金项目
国家自然科学基金资助项目(U20A6003) （U20A6003）
国家自然科学基金广东联合基金资助项目(U1801263,U1701262,U2001201) （U1801263,U1701262,U2001201）
广东省信息物理融合系统重点实验室项目(2020B1212060069) （2020B1212060069）
佛山市重点领域科技攻关项目(2020001006832) （2020001006832）
