Arbitrary-shaped scene text detection by predicting distance map
Abstract Natural scene text detection is a challenging task, and the existing quadrilateral bounding box regression-based methods enable the location of horizontal and multi-oriented texts but have great difficulties in locating arbitrary-shaped texts due to the limited shape of the quadrilateral bo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Xinyu [verfasserIn] |
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| Format: |
E-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 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 52(2022), 12 vom: 07. März, Seite 14374-14386 |
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| Übergeordnetes Werk: |
volume:52 ; year:2022 ; number:12 ; day:07 ; month:03 ; pages:14374-14386 |
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| DOI / URN: |
10.1007/s10489-021-03065-z |
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| Katalog-ID: |
SPR048265454 |
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| 520 | |a Abstract Natural scene text detection is a challenging task, and the existing quadrilateral bounding box regression-based methods enable the location of horizontal and multi-oriented texts but have great difficulties in locating arbitrary-shaped texts due to the limited shape of the quadrilateral bounding box template. Previous segmentation-based methods, which conduct pixel-level classification and separate adjacent texts by predicting center lines with fixed widths, are able to locate the boundaries of arbitrary-shaped texts. However, the detected text regions may stick together or break into multiple areas with sub-optimal results while the width of the center lines is not appropriate. In this paper, a novel natural scene text detector based on distance map is proposed. The method can detect arbitrary-shaped texts more flexibly and robustly by adjusting the width of the center line. Experimental results on several datasets demonstrate that the proposed method is more competitive than the methods based on fixed-width center lines and obtains state-of-the-art or comparable performance on CTW1500, ICDAR2015 and Total-Text. Notably, the proposed method achieves F-measures of 85.4% on the ICDAR 2015 dataset and 81.6% on the Total-Text dataset. Code is available at: https://github.com/Whu-wxy/DistNet. | ||
| 650 | 4 | |a Natural scene |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Arbitrary-shaped text detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Distance map |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Seed fill algorithm |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Yi, Yaohua |0 (orcid)0000-0003-2456-6845 |4 aut | |
| 700 | 1 | |a Peng, Jibing |4 aut | |
| 700 | 1 | |a Wang, Kaili |4 aut | |
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10.1007/s10489-021-03065-z doi (DE-627)SPR048265454 (SPR)s10489-021-03065-z-e DE-627 ger DE-627 rakwb eng Wang, Xinyu verfasserin aut Arbitrary-shaped scene text detection by predicting distance map 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Natural scene text detection is a challenging task, and the existing quadrilateral bounding box regression-based methods enable the location of horizontal and multi-oriented texts but have great difficulties in locating arbitrary-shaped texts due to the limited shape of the quadrilateral bounding box template. Previous segmentation-based methods, which conduct pixel-level classification and separate adjacent texts by predicting center lines with fixed widths, are able to locate the boundaries of arbitrary-shaped texts. However, the detected text regions may stick together or break into multiple areas with sub-optimal results while the width of the center lines is not appropriate. In this paper, a novel natural scene text detector based on distance map is proposed. The method can detect arbitrary-shaped texts more flexibly and robustly by adjusting the width of the center line. Experimental results on several datasets demonstrate that the proposed method is more competitive than the methods based on fixed-width center lines and obtains state-of-the-art or comparable performance on CTW1500, ICDAR2015 and Total-Text. Notably, the proposed method achieves F-measures of 85.4% on the ICDAR 2015 dataset and 81.6% on the Total-Text dataset. Code is available at: https://github.com/Whu-wxy/DistNet. Natural scene (dpeaa)DE-He213 Arbitrary-shaped text detection (dpeaa)DE-He213 Distance map (dpeaa)DE-He213 Seed fill algorithm (dpeaa)DE-He213 Yi, Yaohua (orcid)0000-0003-2456-6845 aut Peng, Jibing aut Wang, Kaili aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 12 vom: 07. März, Seite 14374-14386 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:12 day:07 month:03 pages:14374-14386 https://dx.doi.org/10.1007/s10489-021-03065-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2022 12 07 03 14374-14386 |
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10.1007/s10489-021-03065-z doi (DE-627)SPR048265454 (SPR)s10489-021-03065-z-e DE-627 ger DE-627 rakwb eng Wang, Xinyu verfasserin aut Arbitrary-shaped scene text detection by predicting distance map 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Natural scene text detection is a challenging task, and the existing quadrilateral bounding box regression-based methods enable the location of horizontal and multi-oriented texts but have great difficulties in locating arbitrary-shaped texts due to the limited shape of the quadrilateral bounding box template. Previous segmentation-based methods, which conduct pixel-level classification and separate adjacent texts by predicting center lines with fixed widths, are able to locate the boundaries of arbitrary-shaped texts. However, the detected text regions may stick together or break into multiple areas with sub-optimal results while the width of the center lines is not appropriate. In this paper, a novel natural scene text detector based on distance map is proposed. The method can detect arbitrary-shaped texts more flexibly and robustly by adjusting the width of the center line. Experimental results on several datasets demonstrate that the proposed method is more competitive than the methods based on fixed-width center lines and obtains state-of-the-art or comparable performance on CTW1500, ICDAR2015 and Total-Text. Notably, the proposed method achieves F-measures of 85.4% on the ICDAR 2015 dataset and 81.6% on the Total-Text dataset. Code is available at: https://github.com/Whu-wxy/DistNet. Natural scene (dpeaa)DE-He213 Arbitrary-shaped text detection (dpeaa)DE-He213 Distance map (dpeaa)DE-He213 Seed fill algorithm (dpeaa)DE-He213 Yi, Yaohua (orcid)0000-0003-2456-6845 aut Peng, Jibing aut Wang, Kaili aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 12 vom: 07. März, Seite 14374-14386 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:12 day:07 month:03 pages:14374-14386 https://dx.doi.org/10.1007/s10489-021-03065-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2022 12 07 03 14374-14386 |
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10.1007/s10489-021-03065-z doi (DE-627)SPR048265454 (SPR)s10489-021-03065-z-e DE-627 ger DE-627 rakwb eng Wang, Xinyu verfasserin aut Arbitrary-shaped scene text detection by predicting distance map 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Natural scene text detection is a challenging task, and the existing quadrilateral bounding box regression-based methods enable the location of horizontal and multi-oriented texts but have great difficulties in locating arbitrary-shaped texts due to the limited shape of the quadrilateral bounding box template. Previous segmentation-based methods, which conduct pixel-level classification and separate adjacent texts by predicting center lines with fixed widths, are able to locate the boundaries of arbitrary-shaped texts. However, the detected text regions may stick together or break into multiple areas with sub-optimal results while the width of the center lines is not appropriate. In this paper, a novel natural scene text detector based on distance map is proposed. The method can detect arbitrary-shaped texts more flexibly and robustly by adjusting the width of the center line. Experimental results on several datasets demonstrate that the proposed method is more competitive than the methods based on fixed-width center lines and obtains state-of-the-art or comparable performance on CTW1500, ICDAR2015 and Total-Text. Notably, the proposed method achieves F-measures of 85.4% on the ICDAR 2015 dataset and 81.6% on the Total-Text dataset. Code is available at: https://github.com/Whu-wxy/DistNet. Natural scene (dpeaa)DE-He213 Arbitrary-shaped text detection (dpeaa)DE-He213 Distance map (dpeaa)DE-He213 Seed fill algorithm (dpeaa)DE-He213 Yi, Yaohua (orcid)0000-0003-2456-6845 aut Peng, Jibing aut Wang, Kaili aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 12 vom: 07. März, Seite 14374-14386 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:12 day:07 month:03 pages:14374-14386 https://dx.doi.org/10.1007/s10489-021-03065-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2022 12 07 03 14374-14386 |
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10.1007/s10489-021-03065-z doi (DE-627)SPR048265454 (SPR)s10489-021-03065-z-e DE-627 ger DE-627 rakwb eng Wang, Xinyu verfasserin aut Arbitrary-shaped scene text detection by predicting distance map 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Natural scene text detection is a challenging task, and the existing quadrilateral bounding box regression-based methods enable the location of horizontal and multi-oriented texts but have great difficulties in locating arbitrary-shaped texts due to the limited shape of the quadrilateral bounding box template. Previous segmentation-based methods, which conduct pixel-level classification and separate adjacent texts by predicting center lines with fixed widths, are able to locate the boundaries of arbitrary-shaped texts. However, the detected text regions may stick together or break into multiple areas with sub-optimal results while the width of the center lines is not appropriate. In this paper, a novel natural scene text detector based on distance map is proposed. The method can detect arbitrary-shaped texts more flexibly and robustly by adjusting the width of the center line. Experimental results on several datasets demonstrate that the proposed method is more competitive than the methods based on fixed-width center lines and obtains state-of-the-art or comparable performance on CTW1500, ICDAR2015 and Total-Text. Notably, the proposed method achieves F-measures of 85.4% on the ICDAR 2015 dataset and 81.6% on the Total-Text dataset. Code is available at: https://github.com/Whu-wxy/DistNet. Natural scene (dpeaa)DE-He213 Arbitrary-shaped text detection (dpeaa)DE-He213 Distance map (dpeaa)DE-He213 Seed fill algorithm (dpeaa)DE-He213 Yi, Yaohua (orcid)0000-0003-2456-6845 aut Peng, Jibing aut Wang, Kaili aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 12 vom: 07. März, Seite 14374-14386 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:12 day:07 month:03 pages:14374-14386 https://dx.doi.org/10.1007/s10489-021-03065-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2022 12 07 03 14374-14386 |
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10.1007/s10489-021-03065-z doi (DE-627)SPR048265454 (SPR)s10489-021-03065-z-e DE-627 ger DE-627 rakwb eng Wang, Xinyu verfasserin aut Arbitrary-shaped scene text detection by predicting distance map 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract Natural scene text detection is a challenging task, and the existing quadrilateral bounding box regression-based methods enable the location of horizontal and multi-oriented texts but have great difficulties in locating arbitrary-shaped texts due to the limited shape of the quadrilateral bounding box template. Previous segmentation-based methods, which conduct pixel-level classification and separate adjacent texts by predicting center lines with fixed widths, are able to locate the boundaries of arbitrary-shaped texts. However, the detected text regions may stick together or break into multiple areas with sub-optimal results while the width of the center lines is not appropriate. In this paper, a novel natural scene text detector based on distance map is proposed. The method can detect arbitrary-shaped texts more flexibly and robustly by adjusting the width of the center line. Experimental results on several datasets demonstrate that the proposed method is more competitive than the methods based on fixed-width center lines and obtains state-of-the-art or comparable performance on CTW1500, ICDAR2015 and Total-Text. Notably, the proposed method achieves F-measures of 85.4% on the ICDAR 2015 dataset and 81.6% on the Total-Text dataset. Code is available at: https://github.com/Whu-wxy/DistNet. Natural scene (dpeaa)DE-He213 Arbitrary-shaped text detection (dpeaa)DE-He213 Distance map (dpeaa)DE-He213 Seed fill algorithm (dpeaa)DE-He213 Yi, Yaohua (orcid)0000-0003-2456-6845 aut Peng, Jibing aut Wang, Kaili aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 12 vom: 07. März, Seite 14374-14386 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:12 day:07 month:03 pages:14374-14386 https://dx.doi.org/10.1007/s10489-021-03065-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2022 12 07 03 14374-14386 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR048265454</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509112954.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221002s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10489-021-03065-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR048265454</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10489-021-03065-z-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Xinyu</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Arbitrary-shaped scene text detection by predicting distance map</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Natural scene text detection is a challenging task, and the existing quadrilateral bounding box regression-based methods enable the location of horizontal and multi-oriented texts but have great difficulties in locating arbitrary-shaped texts due to the limited shape of the quadrilateral bounding box template. Previous segmentation-based methods, which conduct pixel-level classification and separate adjacent texts by predicting center lines with fixed widths, are able to locate the boundaries of arbitrary-shaped texts. However, the detected text regions may stick together or break into multiple areas with sub-optimal results while the width of the center lines is not appropriate. In this paper, a novel natural scene text detector based on distance map is proposed. The method can detect arbitrary-shaped texts more flexibly and robustly by adjusting the width of the center line. Experimental results on several datasets demonstrate that the proposed method is more competitive than the methods based on fixed-width center lines and obtains state-of-the-art or comparable performance on CTW1500, ICDAR2015 and Total-Text. Notably, the proposed method achieves F-measures of 85.4% on the ICDAR 2015 dataset and 81.6% on the Total-Text dataset. 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arbitrary-shaped scene text detection by predicting distance map |
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Arbitrary-shaped scene text detection by predicting distance map |
| abstract |
Abstract Natural scene text detection is a challenging task, and the existing quadrilateral bounding box regression-based methods enable the location of horizontal and multi-oriented texts but have great difficulties in locating arbitrary-shaped texts due to the limited shape of the quadrilateral bounding box template. Previous segmentation-based methods, which conduct pixel-level classification and separate adjacent texts by predicting center lines with fixed widths, are able to locate the boundaries of arbitrary-shaped texts. However, the detected text regions may stick together or break into multiple areas with sub-optimal results while the width of the center lines is not appropriate. In this paper, a novel natural scene text detector based on distance map is proposed. The method can detect arbitrary-shaped texts more flexibly and robustly by adjusting the width of the center line. Experimental results on several datasets demonstrate that the proposed method is more competitive than the methods based on fixed-width center lines and obtains state-of-the-art or comparable performance on CTW1500, ICDAR2015 and Total-Text. Notably, the proposed method achieves F-measures of 85.4% on the ICDAR 2015 dataset and 81.6% on the Total-Text dataset. Code is available at: https://github.com/Whu-wxy/DistNet. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract Natural scene text detection is a challenging task, and the existing quadrilateral bounding box regression-based methods enable the location of horizontal and multi-oriented texts but have great difficulties in locating arbitrary-shaped texts due to the limited shape of the quadrilateral bounding box template. Previous segmentation-based methods, which conduct pixel-level classification and separate adjacent texts by predicting center lines with fixed widths, are able to locate the boundaries of arbitrary-shaped texts. However, the detected text regions may stick together or break into multiple areas with sub-optimal results while the width of the center lines is not appropriate. In this paper, a novel natural scene text detector based on distance map is proposed. The method can detect arbitrary-shaped texts more flexibly and robustly by adjusting the width of the center line. Experimental results on several datasets demonstrate that the proposed method is more competitive than the methods based on fixed-width center lines and obtains state-of-the-art or comparable performance on CTW1500, ICDAR2015 and Total-Text. Notably, the proposed method achieves F-measures of 85.4% on the ICDAR 2015 dataset and 81.6% on the Total-Text dataset. Code is available at: https://github.com/Whu-wxy/DistNet. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Natural scene text detection is a challenging task, and the existing quadrilateral bounding box regression-based methods enable the location of horizontal and multi-oriented texts but have great difficulties in locating arbitrary-shaped texts due to the limited shape of the quadrilateral bounding box template. Previous segmentation-based methods, which conduct pixel-level classification and separate adjacent texts by predicting center lines with fixed widths, are able to locate the boundaries of arbitrary-shaped texts. However, the detected text regions may stick together or break into multiple areas with sub-optimal results while the width of the center lines is not appropriate. In this paper, a novel natural scene text detector based on distance map is proposed. The method can detect arbitrary-shaped texts more flexibly and robustly by adjusting the width of the center line. Experimental results on several datasets demonstrate that the proposed method is more competitive than the methods based on fixed-width center lines and obtains state-of-the-art or comparable performance on CTW1500, ICDAR2015 and Total-Text. Notably, the proposed method achieves F-measures of 85.4% on the ICDAR 2015 dataset and 81.6% on the Total-Text dataset. Code is available at: https://github.com/Whu-wxy/DistNet. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| title_short |
Arbitrary-shaped scene text detection by predicting distance map |
| url |
https://dx.doi.org/10.1007/s10489-021-03065-z |
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Yi, Yaohua Peng, Jibing Wang, Kaili |
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Yi, Yaohua Peng, Jibing Wang, Kaili |
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| doi_str |
10.1007/s10489-021-03065-z |
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2024-07-03T18:06:22.401Z |
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| score |
7.400321 |