MOSTL: An Accurate Multi-Oriented Scene Text Localization
Abstract Automatic text localization in natural environments is the main element of many applications including self-driving cars, identifying vehicles, and providing scene information to visually impaired people. However, text in the natural and irregular scene has different degrees in orientations...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Naiemi, Fatemeh [verfasserIn] Ghods, Vahid [verfasserIn] Khalesi, Hassan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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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: Circuits, systems and signal processing - Boston, Mass. : Birkhäuser, 1982, 40(2021), 9 vom: 19. Feb., Seite 4452-4473 |
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| Übergeordnetes Werk: |
volume:40 ; year:2021 ; number:9 ; day:19 ; month:02 ; pages:4452-4473 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00034-021-01674-0 |
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| Katalog-ID: |
SPR044738463 |
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| 520 | |a Abstract Automatic text localization in natural environments is the main element of many applications including self-driving cars, identifying vehicles, and providing scene information to visually impaired people. However, text in the natural and irregular scene has different degrees in orientations, shapes, and colors that make it difficult to detect. In this paper, an accurate multi-oriented scene text localization (MOSTL) is presented to obtain high efficiency of detecting text-based on convolutional neural networks. In the proposed method, an improved ReLU layer (i.ReLU) and an improved inception layer (i.inception) were introduced. Firstly, the proposed structure is used to extract low-level visual features. Then, an extra layer has been used to improve the feature extraction. The i.ReLU and i.inception layers have improved valuable information in text detection. The i.ReLU layers cause to extract some low-level features appropriately. The i.inception layers (specially 3 × 3 convolutions) can obtain broadly varying-sized text more effectively than a linear chain of convolution layer (without inception layers). The output of i.ReLU layers and i.inception layers was fed to an extra layer, which enables MOSTL to detect multi-oriented even curved and vertical texts. We conducted text detection experiments on well-known databases including ICDAR 2019, ICDAR 2017, ICDAR 2015, ICDAR 2003, and MSRA-TD500. MOSTL results yielded performance improvement remarkably. | ||
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| 650 | 4 | |a Convolutional neural network |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Improved inception layer |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Improved ReLU layer |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Curved text |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ghods, Vahid |e verfasserin |4 aut | |
| 700 | 1 | |a Khalesi, Hassan |e verfasserin |4 aut | |
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10.1007/s00034-021-01674-0 doi (DE-627)SPR044738463 (SPR)s00034-021-01674-0-e DE-627 ger DE-627 rakwb eng 620 ASE 600 ASE 53.71 bkl 53.73 bkl 53.13 bkl 50.03 bkl Naiemi, Fatemeh verfasserin aut MOSTL: An Accurate Multi-Oriented Scene Text Localization 2021 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 Automatic text localization in natural environments is the main element of many applications including self-driving cars, identifying vehicles, and providing scene information to visually impaired people. However, text in the natural and irregular scene has different degrees in orientations, shapes, and colors that make it difficult to detect. In this paper, an accurate multi-oriented scene text localization (MOSTL) is presented to obtain high efficiency of detecting text-based on convolutional neural networks. In the proposed method, an improved ReLU layer (i.ReLU) and an improved inception layer (i.inception) were introduced. Firstly, the proposed structure is used to extract low-level visual features. Then, an extra layer has been used to improve the feature extraction. The i.ReLU and i.inception layers have improved valuable information in text detection. The i.ReLU layers cause to extract some low-level features appropriately. The i.inception layers (specially 3 × 3 convolutions) can obtain broadly varying-sized text more effectively than a linear chain of convolution layer (without inception layers). The output of i.ReLU layers and i.inception layers was fed to an extra layer, which enables MOSTL to detect multi-oriented even curved and vertical texts. We conducted text detection experiments on well-known databases including ICDAR 2019, ICDAR 2017, ICDAR 2015, ICDAR 2003, and MSRA-TD500. MOSTL results yielded performance improvement remarkably. Scene text localization (dpeaa)DE-He213 Object detection (dpeaa)DE-He213 Multi-oriented (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Improved inception layer (dpeaa)DE-He213 Improved ReLU layer (dpeaa)DE-He213 Curved text (dpeaa)DE-He213 Ghods, Vahid verfasserin aut Khalesi, Hassan verfasserin aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 40(2021), 9 vom: 19. Feb., Seite 4452-4473 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:40 year:2021 number:9 day:19 month:02 pages:4452-4473 https://dx.doi.org/10.1007/s00034-021-01674-0 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_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_267 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_2119 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 53.71 ASE 53.73 ASE 53.13 ASE 50.03 ASE AR 40 2021 9 19 02 4452-4473 |
| spelling |
10.1007/s00034-021-01674-0 doi (DE-627)SPR044738463 (SPR)s00034-021-01674-0-e DE-627 ger DE-627 rakwb eng 620 ASE 600 ASE 53.71 bkl 53.73 bkl 53.13 bkl 50.03 bkl Naiemi, Fatemeh verfasserin aut MOSTL: An Accurate Multi-Oriented Scene Text Localization 2021 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 Automatic text localization in natural environments is the main element of many applications including self-driving cars, identifying vehicles, and providing scene information to visually impaired people. However, text in the natural and irregular scene has different degrees in orientations, shapes, and colors that make it difficult to detect. In this paper, an accurate multi-oriented scene text localization (MOSTL) is presented to obtain high efficiency of detecting text-based on convolutional neural networks. In the proposed method, an improved ReLU layer (i.ReLU) and an improved inception layer (i.inception) were introduced. Firstly, the proposed structure is used to extract low-level visual features. Then, an extra layer has been used to improve the feature extraction. The i.ReLU and i.inception layers have improved valuable information in text detection. The i.ReLU layers cause to extract some low-level features appropriately. The i.inception layers (specially 3 × 3 convolutions) can obtain broadly varying-sized text more effectively than a linear chain of convolution layer (without inception layers). The output of i.ReLU layers and i.inception layers was fed to an extra layer, which enables MOSTL to detect multi-oriented even curved and vertical texts. We conducted text detection experiments on well-known databases including ICDAR 2019, ICDAR 2017, ICDAR 2015, ICDAR 2003, and MSRA-TD500. MOSTL results yielded performance improvement remarkably. Scene text localization (dpeaa)DE-He213 Object detection (dpeaa)DE-He213 Multi-oriented (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Improved inception layer (dpeaa)DE-He213 Improved ReLU layer (dpeaa)DE-He213 Curved text (dpeaa)DE-He213 Ghods, Vahid verfasserin aut Khalesi, Hassan verfasserin aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 40(2021), 9 vom: 19. Feb., Seite 4452-4473 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:40 year:2021 number:9 day:19 month:02 pages:4452-4473 https://dx.doi.org/10.1007/s00034-021-01674-0 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_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_267 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_2119 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 53.71 ASE 53.73 ASE 53.13 ASE 50.03 ASE AR 40 2021 9 19 02 4452-4473 |
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10.1007/s00034-021-01674-0 doi (DE-627)SPR044738463 (SPR)s00034-021-01674-0-e DE-627 ger DE-627 rakwb eng 620 ASE 600 ASE 53.71 bkl 53.73 bkl 53.13 bkl 50.03 bkl Naiemi, Fatemeh verfasserin aut MOSTL: An Accurate Multi-Oriented Scene Text Localization 2021 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 Automatic text localization in natural environments is the main element of many applications including self-driving cars, identifying vehicles, and providing scene information to visually impaired people. However, text in the natural and irregular scene has different degrees in orientations, shapes, and colors that make it difficult to detect. In this paper, an accurate multi-oriented scene text localization (MOSTL) is presented to obtain high efficiency of detecting text-based on convolutional neural networks. In the proposed method, an improved ReLU layer (i.ReLU) and an improved inception layer (i.inception) were introduced. Firstly, the proposed structure is used to extract low-level visual features. Then, an extra layer has been used to improve the feature extraction. The i.ReLU and i.inception layers have improved valuable information in text detection. The i.ReLU layers cause to extract some low-level features appropriately. The i.inception layers (specially 3 × 3 convolutions) can obtain broadly varying-sized text more effectively than a linear chain of convolution layer (without inception layers). The output of i.ReLU layers and i.inception layers was fed to an extra layer, which enables MOSTL to detect multi-oriented even curved and vertical texts. We conducted text detection experiments on well-known databases including ICDAR 2019, ICDAR 2017, ICDAR 2015, ICDAR 2003, and MSRA-TD500. MOSTL results yielded performance improvement remarkably. Scene text localization (dpeaa)DE-He213 Object detection (dpeaa)DE-He213 Multi-oriented (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Improved inception layer (dpeaa)DE-He213 Improved ReLU layer (dpeaa)DE-He213 Curved text (dpeaa)DE-He213 Ghods, Vahid verfasserin aut Khalesi, Hassan verfasserin aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 40(2021), 9 vom: 19. Feb., Seite 4452-4473 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:40 year:2021 number:9 day:19 month:02 pages:4452-4473 https://dx.doi.org/10.1007/s00034-021-01674-0 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_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_267 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_2119 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 53.71 ASE 53.73 ASE 53.13 ASE 50.03 ASE AR 40 2021 9 19 02 4452-4473 |
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10.1007/s00034-021-01674-0 doi (DE-627)SPR044738463 (SPR)s00034-021-01674-0-e DE-627 ger DE-627 rakwb eng 620 ASE 600 ASE 53.71 bkl 53.73 bkl 53.13 bkl 50.03 bkl Naiemi, Fatemeh verfasserin aut MOSTL: An Accurate Multi-Oriented Scene Text Localization 2021 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 Automatic text localization in natural environments is the main element of many applications including self-driving cars, identifying vehicles, and providing scene information to visually impaired people. However, text in the natural and irregular scene has different degrees in orientations, shapes, and colors that make it difficult to detect. In this paper, an accurate multi-oriented scene text localization (MOSTL) is presented to obtain high efficiency of detecting text-based on convolutional neural networks. In the proposed method, an improved ReLU layer (i.ReLU) and an improved inception layer (i.inception) were introduced. Firstly, the proposed structure is used to extract low-level visual features. Then, an extra layer has been used to improve the feature extraction. The i.ReLU and i.inception layers have improved valuable information in text detection. The i.ReLU layers cause to extract some low-level features appropriately. The i.inception layers (specially 3 × 3 convolutions) can obtain broadly varying-sized text more effectively than a linear chain of convolution layer (without inception layers). The output of i.ReLU layers and i.inception layers was fed to an extra layer, which enables MOSTL to detect multi-oriented even curved and vertical texts. We conducted text detection experiments on well-known databases including ICDAR 2019, ICDAR 2017, ICDAR 2015, ICDAR 2003, and MSRA-TD500. MOSTL results yielded performance improvement remarkably. Scene text localization (dpeaa)DE-He213 Object detection (dpeaa)DE-He213 Multi-oriented (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Improved inception layer (dpeaa)DE-He213 Improved ReLU layer (dpeaa)DE-He213 Curved text (dpeaa)DE-He213 Ghods, Vahid verfasserin aut Khalesi, Hassan verfasserin aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 40(2021), 9 vom: 19. Feb., Seite 4452-4473 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:40 year:2021 number:9 day:19 month:02 pages:4452-4473 https://dx.doi.org/10.1007/s00034-021-01674-0 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_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_267 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_2119 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 53.71 ASE 53.73 ASE 53.13 ASE 50.03 ASE AR 40 2021 9 19 02 4452-4473 |
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10.1007/s00034-021-01674-0 doi (DE-627)SPR044738463 (SPR)s00034-021-01674-0-e DE-627 ger DE-627 rakwb eng 620 ASE 600 ASE 53.71 bkl 53.73 bkl 53.13 bkl 50.03 bkl Naiemi, Fatemeh verfasserin aut MOSTL: An Accurate Multi-Oriented Scene Text Localization 2021 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 Automatic text localization in natural environments is the main element of many applications including self-driving cars, identifying vehicles, and providing scene information to visually impaired people. However, text in the natural and irregular scene has different degrees in orientations, shapes, and colors that make it difficult to detect. In this paper, an accurate multi-oriented scene text localization (MOSTL) is presented to obtain high efficiency of detecting text-based on convolutional neural networks. In the proposed method, an improved ReLU layer (i.ReLU) and an improved inception layer (i.inception) were introduced. Firstly, the proposed structure is used to extract low-level visual features. Then, an extra layer has been used to improve the feature extraction. The i.ReLU and i.inception layers have improved valuable information in text detection. The i.ReLU layers cause to extract some low-level features appropriately. The i.inception layers (specially 3 × 3 convolutions) can obtain broadly varying-sized text more effectively than a linear chain of convolution layer (without inception layers). The output of i.ReLU layers and i.inception layers was fed to an extra layer, which enables MOSTL to detect multi-oriented even curved and vertical texts. We conducted text detection experiments on well-known databases including ICDAR 2019, ICDAR 2017, ICDAR 2015, ICDAR 2003, and MSRA-TD500. MOSTL results yielded performance improvement remarkably. Scene text localization (dpeaa)DE-He213 Object detection (dpeaa)DE-He213 Multi-oriented (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Improved inception layer (dpeaa)DE-He213 Improved ReLU layer (dpeaa)DE-He213 Curved text (dpeaa)DE-He213 Ghods, Vahid verfasserin aut Khalesi, Hassan verfasserin aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 40(2021), 9 vom: 19. Feb., Seite 4452-4473 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:40 year:2021 number:9 day:19 month:02 pages:4452-4473 https://dx.doi.org/10.1007/s00034-021-01674-0 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_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_267 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_2119 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 53.71 ASE 53.73 ASE 53.13 ASE 50.03 ASE AR 40 2021 9 19 02 4452-4473 |
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Enthalten in Circuits, systems and signal processing 40(2021), 9 vom: 19. Feb., Seite 4452-4473 volume:40 year:2021 number:9 day:19 month:02 pages:4452-4473 |
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Scene text localization Object detection Multi-oriented Convolutional neural network Improved inception layer Improved ReLU layer Curved text |
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However, text in the natural and irregular scene has different degrees in orientations, shapes, and colors that make it difficult to detect. In this paper, an accurate multi-oriented scene text localization (MOSTL) is presented to obtain high efficiency of detecting text-based on convolutional neural networks. In the proposed method, an improved ReLU layer (i.ReLU) and an improved inception layer (i.inception) were introduced. Firstly, the proposed structure is used to extract low-level visual features. Then, an extra layer has been used to improve the feature extraction. The i.ReLU and i.inception layers have improved valuable information in text detection. The i.ReLU layers cause to extract some low-level features appropriately. The i.inception layers (specially 3 × 3 convolutions) can obtain broadly varying-sized text more effectively than a linear chain of convolution layer (without inception layers). The output of i.ReLU layers and i.inception layers was fed to an extra layer, which enables MOSTL to detect multi-oriented even curved and vertical texts. We conducted text detection experiments on well-known databases including ICDAR 2019, ICDAR 2017, ICDAR 2015, ICDAR 2003, and MSRA-TD500. 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Naiemi, Fatemeh |
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Naiemi, Fatemeh ddc 620 ddc 600 bkl 53.71 bkl 53.73 bkl 53.13 bkl 50.03 misc Scene text localization misc Object detection misc Multi-oriented misc Convolutional neural network misc Improved inception layer misc Improved ReLU layer misc Curved text MOSTL: An Accurate Multi-Oriented Scene Text Localization |
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620 ASE 600 ASE 53.71 bkl 53.73 bkl 53.13 bkl 50.03 bkl MOSTL: An Accurate Multi-Oriented Scene Text Localization Scene text localization (dpeaa)DE-He213 Object detection (dpeaa)DE-He213 Multi-oriented (dpeaa)DE-He213 Convolutional neural network (dpeaa)DE-He213 Improved inception layer (dpeaa)DE-He213 Improved ReLU layer (dpeaa)DE-He213 Curved text (dpeaa)DE-He213 |
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ddc 620 ddc 600 bkl 53.71 bkl 53.73 bkl 53.13 bkl 50.03 misc Scene text localization misc Object detection misc Multi-oriented misc Convolutional neural network misc Improved inception layer misc Improved ReLU layer misc Curved text |
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ddc 620 ddc 600 bkl 53.71 bkl 53.73 bkl 53.13 bkl 50.03 misc Scene text localization misc Object detection misc Multi-oriented misc Convolutional neural network misc Improved inception layer misc Improved ReLU layer misc Curved text |
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Naiemi, Fatemeh Ghods, Vahid Khalesi, Hassan |
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Naiemi, Fatemeh |
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mostl: an accurate multi-oriented scene text localization |
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MOSTL: An Accurate Multi-Oriented Scene Text Localization |
| abstract |
Abstract Automatic text localization in natural environments is the main element of many applications including self-driving cars, identifying vehicles, and providing scene information to visually impaired people. However, text in the natural and irregular scene has different degrees in orientations, shapes, and colors that make it difficult to detect. In this paper, an accurate multi-oriented scene text localization (MOSTL) is presented to obtain high efficiency of detecting text-based on convolutional neural networks. In the proposed method, an improved ReLU layer (i.ReLU) and an improved inception layer (i.inception) were introduced. Firstly, the proposed structure is used to extract low-level visual features. Then, an extra layer has been used to improve the feature extraction. The i.ReLU and i.inception layers have improved valuable information in text detection. The i.ReLU layers cause to extract some low-level features appropriately. The i.inception layers (specially 3 × 3 convolutions) can obtain broadly varying-sized text more effectively than a linear chain of convolution layer (without inception layers). The output of i.ReLU layers and i.inception layers was fed to an extra layer, which enables MOSTL to detect multi-oriented even curved and vertical texts. We conducted text detection experiments on well-known databases including ICDAR 2019, ICDAR 2017, ICDAR 2015, ICDAR 2003, and MSRA-TD500. MOSTL results yielded performance improvement remarkably. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstractGer |
Abstract Automatic text localization in natural environments is the main element of many applications including self-driving cars, identifying vehicles, and providing scene information to visually impaired people. However, text in the natural and irregular scene has different degrees in orientations, shapes, and colors that make it difficult to detect. In this paper, an accurate multi-oriented scene text localization (MOSTL) is presented to obtain high efficiency of detecting text-based on convolutional neural networks. In the proposed method, an improved ReLU layer (i.ReLU) and an improved inception layer (i.inception) were introduced. Firstly, the proposed structure is used to extract low-level visual features. Then, an extra layer has been used to improve the feature extraction. The i.ReLU and i.inception layers have improved valuable information in text detection. The i.ReLU layers cause to extract some low-level features appropriately. The i.inception layers (specially 3 × 3 convolutions) can obtain broadly varying-sized text more effectively than a linear chain of convolution layer (without inception layers). The output of i.ReLU layers and i.inception layers was fed to an extra layer, which enables MOSTL to detect multi-oriented even curved and vertical texts. We conducted text detection experiments on well-known databases including ICDAR 2019, ICDAR 2017, ICDAR 2015, ICDAR 2003, and MSRA-TD500. MOSTL results yielded performance improvement remarkably. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract Automatic text localization in natural environments is the main element of many applications including self-driving cars, identifying vehicles, and providing scene information to visually impaired people. However, text in the natural and irregular scene has different degrees in orientations, shapes, and colors that make it difficult to detect. In this paper, an accurate multi-oriented scene text localization (MOSTL) is presented to obtain high efficiency of detecting text-based on convolutional neural networks. In the proposed method, an improved ReLU layer (i.ReLU) and an improved inception layer (i.inception) were introduced. Firstly, the proposed structure is used to extract low-level visual features. Then, an extra layer has been used to improve the feature extraction. The i.ReLU and i.inception layers have improved valuable information in text detection. The i.ReLU layers cause to extract some low-level features appropriately. The i.inception layers (specially 3 × 3 convolutions) can obtain broadly varying-sized text more effectively than a linear chain of convolution layer (without inception layers). The output of i.ReLU layers and i.inception layers was fed to an extra layer, which enables MOSTL to detect multi-oriented even curved and vertical texts. We conducted text detection experiments on well-known databases including ICDAR 2019, ICDAR 2017, ICDAR 2015, ICDAR 2003, and MSRA-TD500. MOSTL results yielded performance improvement remarkably. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021 |
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MOSTL: An Accurate Multi-Oriented Scene Text Localization |
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| score |
7.398711 |