CSFF-Net: Scene Text Detection Based on Cross-Scale Feature Fusion
In the last years, methods for detecting text in real scenes have made significant progress with an increase in neural networks. However, due to the limitation of the receptive field of the central nervous system and the simple representation of text by using rectangular bounding boxes, the previous...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/12/12/524 |
| _version_ | 1797503677419749376 |
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| author | Yuan Li Mayire Ibrayim Askar Hamdulla |
| author_facet | Yuan Li Mayire Ibrayim Askar Hamdulla |
| author_sort | Yuan Li |
| collection | DOAJ |
| description | In the last years, methods for detecting text in real scenes have made significant progress with an increase in neural networks. However, due to the limitation of the receptive field of the central nervous system and the simple representation of text by using rectangular bounding boxes, the previous methods may be insufficient for working with more challenging instances of text. To solve this problem, this paper proposes a scene text detection network based on cross-scale feature fusion (CSFF-Net). The framework is based on the lightweight backbone network Resnet, and the feature learning is enhanced by embedding the depth weighted convolution module (DWCM) while retaining the original feature information extracted by CNN. At the same time, the 3D-Attention module is also introduced to merge the context information of adjacent areas, so as to refine the features in each spatial size. In addition, because the Feature Pyramid Network (FPN) cannot completely solve the interdependence problem by simple element-wise addition to process cross-layer information flow, this paper introduces a Cross-Level Feature Fusion Module (CLFFM) based on FPN, which is called Cross-Level Feature Pyramid Network (Cross-Level FPN). The proposed CLFFM can better handle cross-layer information flow and output detailed feature information, thus improving the accuracy of text region detection. Compared to the original network framework, the framework provides a more advanced performance in detecting text images of complex scenes, and extensive experiments on three challenging datasets validate the realizability of our approach. |
| first_indexed | 2024-03-10T03:54:08Z |
| format | Article |
| id | doaj.art-e02ab6e84d0e494791f4ebcd2f32a820 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T03:54:08Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-e02ab6e84d0e494791f4ebcd2f32a8202023-11-23T08:51:43ZengMDPI AGInformation2078-24892021-12-01121252410.3390/info12120524CSFF-Net: Scene Text Detection Based on Cross-Scale Feature FusionYuan Li0Mayire Ibrayim1Askar Hamdulla2College of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaCollege of Information Science and Engineering, Xinjiang University, Urumqi 830046, ChinaIn the last years, methods for detecting text in real scenes have made significant progress with an increase in neural networks. However, due to the limitation of the receptive field of the central nervous system and the simple representation of text by using rectangular bounding boxes, the previous methods may be insufficient for working with more challenging instances of text. To solve this problem, this paper proposes a scene text detection network based on cross-scale feature fusion (CSFF-Net). The framework is based on the lightweight backbone network Resnet, and the feature learning is enhanced by embedding the depth weighted convolution module (DWCM) while retaining the original feature information extracted by CNN. At the same time, the 3D-Attention module is also introduced to merge the context information of adjacent areas, so as to refine the features in each spatial size. In addition, because the Feature Pyramid Network (FPN) cannot completely solve the interdependence problem by simple element-wise addition to process cross-layer information flow, this paper introduces a Cross-Level Feature Fusion Module (CLFFM) based on FPN, which is called Cross-Level Feature Pyramid Network (Cross-Level FPN). The proposed CLFFM can better handle cross-layer information flow and output detailed feature information, thus improving the accuracy of text region detection. Compared to the original network framework, the framework provides a more advanced performance in detecting text images of complex scenes, and extensive experiments on three challenging datasets validate the realizability of our approach.https://www.mdpi.com/2078-2489/12/12/524feature extractionattention mechanismpyramid networkdeep learningtext detection |
| spellingShingle | Yuan Li Mayire Ibrayim Askar Hamdulla CSFF-Net: Scene Text Detection Based on Cross-Scale Feature Fusion Information feature extraction attention mechanism pyramid network deep learning text detection |
| title | CSFF-Net: Scene Text Detection Based on Cross-Scale Feature Fusion |
| title_full | CSFF-Net: Scene Text Detection Based on Cross-Scale Feature Fusion |
| title_fullStr | CSFF-Net: Scene Text Detection Based on Cross-Scale Feature Fusion |
| title_full_unstemmed | CSFF-Net: Scene Text Detection Based on Cross-Scale Feature Fusion |
| title_short | CSFF-Net: Scene Text Detection Based on Cross-Scale Feature Fusion |
| title_sort | csff net scene text detection based on cross scale feature fusion |
| topic | feature extraction attention mechanism pyramid network deep learning text detection |
| url | https://www.mdpi.com/2078-2489/12/12/524 |
| work_keys_str_mv | AT yuanli csffnetscenetextdetectionbasedoncrossscalefeaturefusion AT mayireibrayim csffnetscenetextdetectionbasedoncrossscalefeaturefusion AT askarhamdulla csffnetscenetextdetectionbasedoncrossscalefeaturefusion |