Scene Text Detection Using HRNet and Spatial Attention Mechanism
To better extract the features from text instances with various shapes, a scene text detector using High Resolution Net (HRNet) and spatial attention mechanism is proposed in this paper. Specifically, we use HRNetv2-W18 as the backbone network to extract the text feature in text instances with compl...
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| Vydáno v: | Programming and computer software Ročník 49; číslo 8; s. 954 - 965 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Moscow
Pleiades Publishing
01.12.2023
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| ISSN: | 0361-7688, 1608-3261 |
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| Abstract | To better extract the features from text instances with various shapes, a scene text detector using High Resolution Net (HRNet) and spatial attention mechanism is proposed in this paper. Specifically, we use HRNetv2-W18 as the backbone network to extract the text feature in text instances with complex shapes. Considering that the scene text instance is usually small, to avoid too small feature size, we optimize HRNet through deformable convolution and Smooth Maximum Unit (SMU) activation function, so that the network can retain more detail information and location information of the text instance. In addition, a Text Region Attention Module (TRAM) is added after the backbone to make it pay more attention to the text location information and a loss function is used to TRAM, so that the network can learn the features better. The experimental results illustrate that the proposed method can compete with the state-of-the-art methods. Code is available at:
https://github.com/zhangyan1005/HR-DBNet
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| AbstractList | To better extract the features from text instances with various shapes, a scene text detector using High Resolution Net (HRNet) and spatial attention mechanism is proposed in this paper. Specifically, we use HRNetv2-W18 as the backbone network to extract the text feature in text instances with complex shapes. Considering that the scene text instance is usually small, to avoid too small feature size, we optimize HRNet through deformable convolution and Smooth Maximum Unit (SMU) activation function, so that the network can retain more detail information and location information of the text instance. In addition, a Text Region Attention Module (TRAM) is added after the backbone to make it pay more attention to the text location information and a loss function is used to TRAM, so that the network can learn the features better. The experimental results illustrate that the proposed method can compete with the state-of-the-art methods. Code is available at:
https://github.com/zhangyan1005/HR-DBNet
. |
| Author | Guo, Jinting Jiang, Wuming Tang, Qingsong Pan, Bolin Jiang, Zhangyan |
| Author_xml | – sequence: 1 givenname: Qingsong surname: Tang fullname: Tang, Qingsong email: tangqs@mail.neu.edu.cn organization: Department of Mathematics, College of Sciences, Northeastern University – sequence: 2 givenname: Zhangyan surname: Jiang fullname: Jiang, Zhangyan email: 1714203863@qq.com organization: Department of Mathematics, College of Sciences, Northeastern University – sequence: 3 givenname: Bolin surname: Pan fullname: Pan, Bolin email: 2100126@stu.neu.edu.cn organization: Department of Mathematics, College of Sciences, Northeastern University – sequence: 4 givenname: Jinting surname: Guo fullname: Guo, Jinting email: 2100107@stu.neu.edu.cn organization: Department of Mathematics, College of Sciences, Northeastern University – sequence: 5 givenname: Wuming surname: Jiang fullname: Jiang, Wuming email: jiangwuming@eyecool.cn organization: Beijing Eyecool Technology Co., Ltd |
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