摘要
基于深度学习的检测方法在文本形状较规则的情况下,已经取得较好的检测结果,但对于倾斜以及弯曲的文本行仍有改进空间。文章在渐进式尺度扩展网络PSENet的基础上,通过使用Res2Net模块提取多尺度特征,并结合全局卷积网络GCN进行特征融合,来对原有模型进行改进。根据在SCUT-CTW1500和Total-Text数据集的实验结果对比,证明改进的算法有效可行。
The detection method based on deep learning has achieved good results in the case of regular text shape,but there is still an improved space for tilting and curved text.Based on the progressive scale expansion network PSENet,the original model is improved by using the Res2Net module to extract multi-scale features and combined with the global convolution network GCN.According to the comparison of experimental results in SCUT-CTW1500 and Total-Text data sets,the effectiveness of the improved algorithm is proved.
作者
彭栋
支世尧
李盛达
杨鹏
Peng Dong;Zhi Shiyao;Li Shengda;Yang Peng(School of Information Engineering,Nanjing Audit University,Nanjing,Jiangsu 211815,China)
出处
《计算机时代》
2022年第6期89-92,96,共5页
Computer Era
基金
江苏省自然科学基金“基于Top-Down方法的场景文字检测模型设计与优化”(BK20211294)。
关键词
文本检测
语义分割
多尺度
全局卷积
text detection
semantic segmentation
multi-scale
global convolution