A spatial feature adaptive network for text detection
Abstract Due to the capacity of detection arbitrary shapes of text and the robustness in practical applications, scene text detection methods based on segmentation have attached more attention. More accurate segmentation and better feature extraction are the core of segmentation-based detection. In...
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Gespeichert in:
| Autor*in: |
Tang, Qingsong [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 81(2022), 11 vom: 28. Feb., Seite 15285-15302 |
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volume:81 ; year:2022 ; number:11 ; day:28 ; month:02 ; pages:15285-15302 |
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| DOI / URN: |
10.1007/s11042-022-12619-3 |
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| 520 | |a Abstract Due to the capacity of detection arbitrary shapes of text and the robustness in practical applications, scene text detection methods based on segmentation have attached more attention. More accurate segmentation and better feature extraction are the core of segmentation-based detection. In order to refine the result of segmentation, we replace the convolution in the first block of the ResNet50 by desubpixel convolution to enhance the feature extraction capabilities of the network. We also propose a spatial adaptive convolutional network to adjust the features extracted by the backbone so that the network can extract features more suitable for natural scene text detection. We implement the presented network based on PSENet. The results on ICDAR2015 and SCUT-CTW1500 demonstrate that our module can improve the performance of text detection. The precision, recall and F-measure have reached 87.27%, 84.88% and 86.06% on ICDAR2015. And they have reached 81.99%, 82.63% and 82.31% on CTW1500. Our code will be available at https://github.com/fengdashuai/Ada-PSENet. | ||
| 650 | 4 | |a Text detection | |
| 650 | 4 | |a Desubpixel convolution | |
| 650 | 4 | |a Convolution neural network | |
| 650 | 4 | |a Spatial adaptive convolutional network | |
| 700 | 1 | |a Feng, Xiaoxu |4 aut | |
| 700 | 1 | |a Zhang, Xiangde |4 aut | |
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10.1007/s11042-022-12619-3 doi (DE-627)OLC2078570826 (DE-He213)s11042-022-12619-3-p DE-627 ger DE-627 rakwb eng 070 004 VZ Tang, Qingsong verfasserin aut A spatial feature adaptive network for text detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Due to the capacity of detection arbitrary shapes of text and the robustness in practical applications, scene text detection methods based on segmentation have attached more attention. More accurate segmentation and better feature extraction are the core of segmentation-based detection. In order to refine the result of segmentation, we replace the convolution in the first block of the ResNet50 by desubpixel convolution to enhance the feature extraction capabilities of the network. We also propose a spatial adaptive convolutional network to adjust the features extracted by the backbone so that the network can extract features more suitable for natural scene text detection. We implement the presented network based on PSENet. The results on ICDAR2015 and SCUT-CTW1500 demonstrate that our module can improve the performance of text detection. The precision, recall and F-measure have reached 87.27%, 84.88% and 86.06% on ICDAR2015. And they have reached 81.99%, 82.63% and 82.31% on CTW1500. Our code will be available at https://github.com/fengdashuai/Ada-PSENet. Text detection Desubpixel convolution Convolution neural network Spatial adaptive convolutional network Feng, Xiaoxu aut Zhang, Xiangde aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 11 vom: 28. Feb., Seite 15285-15302 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:11 day:28 month:02 pages:15285-15302 https://doi.org/10.1007/s11042-022-12619-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 11 28 02 15285-15302 |
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10.1007/s11042-022-12619-3 doi (DE-627)OLC2078570826 (DE-He213)s11042-022-12619-3-p DE-627 ger DE-627 rakwb eng 070 004 VZ Tang, Qingsong verfasserin aut A spatial feature adaptive network for text detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Due to the capacity of detection arbitrary shapes of text and the robustness in practical applications, scene text detection methods based on segmentation have attached more attention. More accurate segmentation and better feature extraction are the core of segmentation-based detection. In order to refine the result of segmentation, we replace the convolution in the first block of the ResNet50 by desubpixel convolution to enhance the feature extraction capabilities of the network. We also propose a spatial adaptive convolutional network to adjust the features extracted by the backbone so that the network can extract features more suitable for natural scene text detection. We implement the presented network based on PSENet. The results on ICDAR2015 and SCUT-CTW1500 demonstrate that our module can improve the performance of text detection. The precision, recall and F-measure have reached 87.27%, 84.88% and 86.06% on ICDAR2015. And they have reached 81.99%, 82.63% and 82.31% on CTW1500. Our code will be available at https://github.com/fengdashuai/Ada-PSENet. Text detection Desubpixel convolution Convolution neural network Spatial adaptive convolutional network Feng, Xiaoxu aut Zhang, Xiangde aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 11 vom: 28. Feb., Seite 15285-15302 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:11 day:28 month:02 pages:15285-15302 https://doi.org/10.1007/s11042-022-12619-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 11 28 02 15285-15302 |
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10.1007/s11042-022-12619-3 doi (DE-627)OLC2078570826 (DE-He213)s11042-022-12619-3-p DE-627 ger DE-627 rakwb eng 070 004 VZ Tang, Qingsong verfasserin aut A spatial feature adaptive network for text detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Due to the capacity of detection arbitrary shapes of text and the robustness in practical applications, scene text detection methods based on segmentation have attached more attention. More accurate segmentation and better feature extraction are the core of segmentation-based detection. In order to refine the result of segmentation, we replace the convolution in the first block of the ResNet50 by desubpixel convolution to enhance the feature extraction capabilities of the network. We also propose a spatial adaptive convolutional network to adjust the features extracted by the backbone so that the network can extract features more suitable for natural scene text detection. We implement the presented network based on PSENet. The results on ICDAR2015 and SCUT-CTW1500 demonstrate that our module can improve the performance of text detection. The precision, recall and F-measure have reached 87.27%, 84.88% and 86.06% on ICDAR2015. And they have reached 81.99%, 82.63% and 82.31% on CTW1500. Our code will be available at https://github.com/fengdashuai/Ada-PSENet. Text detection Desubpixel convolution Convolution neural network Spatial adaptive convolutional network Feng, Xiaoxu aut Zhang, Xiangde aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 11 vom: 28. Feb., Seite 15285-15302 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:11 day:28 month:02 pages:15285-15302 https://doi.org/10.1007/s11042-022-12619-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 11 28 02 15285-15302 |
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10.1007/s11042-022-12619-3 doi (DE-627)OLC2078570826 (DE-He213)s11042-022-12619-3-p DE-627 ger DE-627 rakwb eng 070 004 VZ Tang, Qingsong verfasserin aut A spatial feature adaptive network for text detection 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Due to the capacity of detection arbitrary shapes of text and the robustness in practical applications, scene text detection methods based on segmentation have attached more attention. More accurate segmentation and better feature extraction are the core of segmentation-based detection. In order to refine the result of segmentation, we replace the convolution in the first block of the ResNet50 by desubpixel convolution to enhance the feature extraction capabilities of the network. We also propose a spatial adaptive convolutional network to adjust the features extracted by the backbone so that the network can extract features more suitable for natural scene text detection. We implement the presented network based on PSENet. The results on ICDAR2015 and SCUT-CTW1500 demonstrate that our module can improve the performance of text detection. The precision, recall and F-measure have reached 87.27%, 84.88% and 86.06% on ICDAR2015. And they have reached 81.99%, 82.63% and 82.31% on CTW1500. Our code will be available at https://github.com/fengdashuai/Ada-PSENet. Text detection Desubpixel convolution Convolution neural network Spatial adaptive convolutional network Feng, Xiaoxu aut Zhang, Xiangde aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 11 vom: 28. Feb., Seite 15285-15302 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:11 day:28 month:02 pages:15285-15302 https://doi.org/10.1007/s11042-022-12619-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 11 28 02 15285-15302 |
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Abstract Due to the capacity of detection arbitrary shapes of text and the robustness in practical applications, scene text detection methods based on segmentation have attached more attention. More accurate segmentation and better feature extraction are the core of segmentation-based detection. In order to refine the result of segmentation, we replace the convolution in the first block of the ResNet50 by desubpixel convolution to enhance the feature extraction capabilities of the network. We also propose a spatial adaptive convolutional network to adjust the features extracted by the backbone so that the network can extract features more suitable for natural scene text detection. We implement the presented network based on PSENet. The results on ICDAR2015 and SCUT-CTW1500 demonstrate that our module can improve the performance of text detection. The precision, recall and F-measure have reached 87.27%, 84.88% and 86.06% on ICDAR2015. And they have reached 81.99%, 82.63% and 82.31% on CTW1500. Our code will be available at https://github.com/fengdashuai/Ada-PSENet. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract Due to the capacity of detection arbitrary shapes of text and the robustness in practical applications, scene text detection methods based on segmentation have attached more attention. More accurate segmentation and better feature extraction are the core of segmentation-based detection. In order to refine the result of segmentation, we replace the convolution in the first block of the ResNet50 by desubpixel convolution to enhance the feature extraction capabilities of the network. We also propose a spatial adaptive convolutional network to adjust the features extracted by the backbone so that the network can extract features more suitable for natural scene text detection. We implement the presented network based on PSENet. The results on ICDAR2015 and SCUT-CTW1500 demonstrate that our module can improve the performance of text detection. The precision, recall and F-measure have reached 87.27%, 84.88% and 86.06% on ICDAR2015. And they have reached 81.99%, 82.63% and 82.31% on CTW1500. Our code will be available at https://github.com/fengdashuai/Ada-PSENet. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract Due to the capacity of detection arbitrary shapes of text and the robustness in practical applications, scene text detection methods based on segmentation have attached more attention. More accurate segmentation and better feature extraction are the core of segmentation-based detection. In order to refine the result of segmentation, we replace the convolution in the first block of the ResNet50 by desubpixel convolution to enhance the feature extraction capabilities of the network. We also propose a spatial adaptive convolutional network to adjust the features extracted by the backbone so that the network can extract features more suitable for natural scene text detection. We implement the presented network based on PSENet. The results on ICDAR2015 and SCUT-CTW1500 demonstrate that our module can improve the performance of text detection. The precision, recall and F-measure have reached 87.27%, 84.88% and 86.06% on ICDAR2015. And they have reached 81.99%, 82.63% and 82.31% on CTW1500. Our code will be available at https://github.com/fengdashuai/Ada-PSENet. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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A spatial feature adaptive network for text detection |
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Feng, Xiaoxu Zhang, Xiangde |
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