Scene text recognition using residual convolutional recurrent neural network
Abstract Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural ne...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lei, Zhengchao [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Machine vision and applications - Springer Berlin Heidelberg, 1988, 29(2018), 5 vom: 16. Juni, Seite 861-871 |
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| Übergeordnetes Werk: |
volume:29 ; year:2018 ; number:5 ; day:16 ; month:06 ; pages:861-871 |
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| DOI / URN: |
10.1007/s00138-018-0942-y |
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| 520 | |a Abstract Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural network (CRNN) is realized by combining convolutional neural network (CNN) with recurrent neural network (RNN). The CNN part extracts features and the RNN part encodes and decodes feature sequences. In order to improve the accuracy rate of scene text recognition based on CRNN, we explore different deeper CNN architectures to get feature descriptors and analyze the corresponding text recognition results. Specifically, VGG and ResNet are introduced to train these different deep models and obtain the encoding information of images. The experimental results on public datasets demonstrate the effectiveness of our method. | ||
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| 700 | 1 | |a Shen, Jianbing |4 aut | |
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10.1007/s00138-018-0942-y doi (DE-627)OLC2074632053 (DE-He213)s00138-018-0942-y-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Lei, Zhengchao verfasserin aut Scene text recognition using residual convolutional recurrent neural network 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural network (CRNN) is realized by combining convolutional neural network (CNN) with recurrent neural network (RNN). The CNN part extracts features and the RNN part encodes and decodes feature sequences. In order to improve the accuracy rate of scene text recognition based on CRNN, we explore different deeper CNN architectures to get feature descriptors and analyze the corresponding text recognition results. Specifically, VGG and ResNet are introduced to train these different deep models and obtain the encoding information of images. The experimental results on public datasets demonstrate the effectiveness of our method. Residual convolutional recurrent neural network Scene text recognition Convolutional neural network Recurrent neural network Residual network Zhao, Sanyuan aut Song, Hongmei aut Shen, Jianbing aut Enthalten in Machine vision and applications Springer Berlin Heidelberg, 1988 29(2018), 5 vom: 16. Juni, Seite 861-871 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:29 year:2018 number:5 day:16 month:06 pages:861-871 https://doi.org/10.1007/s00138-018-0942-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 29 2018 5 16 06 861-871 |
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10.1007/s00138-018-0942-y doi (DE-627)OLC2074632053 (DE-He213)s00138-018-0942-y-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Lei, Zhengchao verfasserin aut Scene text recognition using residual convolutional recurrent neural network 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural network (CRNN) is realized by combining convolutional neural network (CNN) with recurrent neural network (RNN). The CNN part extracts features and the RNN part encodes and decodes feature sequences. In order to improve the accuracy rate of scene text recognition based on CRNN, we explore different deeper CNN architectures to get feature descriptors and analyze the corresponding text recognition results. Specifically, VGG and ResNet are introduced to train these different deep models and obtain the encoding information of images. The experimental results on public datasets demonstrate the effectiveness of our method. Residual convolutional recurrent neural network Scene text recognition Convolutional neural network Recurrent neural network Residual network Zhao, Sanyuan aut Song, Hongmei aut Shen, Jianbing aut Enthalten in Machine vision and applications Springer Berlin Heidelberg, 1988 29(2018), 5 vom: 16. Juni, Seite 861-871 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:29 year:2018 number:5 day:16 month:06 pages:861-871 https://doi.org/10.1007/s00138-018-0942-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 29 2018 5 16 06 861-871 |
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10.1007/s00138-018-0942-y doi (DE-627)OLC2074632053 (DE-He213)s00138-018-0942-y-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Lei, Zhengchao verfasserin aut Scene text recognition using residual convolutional recurrent neural network 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural network (CRNN) is realized by combining convolutional neural network (CNN) with recurrent neural network (RNN). The CNN part extracts features and the RNN part encodes and decodes feature sequences. In order to improve the accuracy rate of scene text recognition based on CRNN, we explore different deeper CNN architectures to get feature descriptors and analyze the corresponding text recognition results. Specifically, VGG and ResNet are introduced to train these different deep models and obtain the encoding information of images. The experimental results on public datasets demonstrate the effectiveness of our method. Residual convolutional recurrent neural network Scene text recognition Convolutional neural network Recurrent neural network Residual network Zhao, Sanyuan aut Song, Hongmei aut Shen, Jianbing aut Enthalten in Machine vision and applications Springer Berlin Heidelberg, 1988 29(2018), 5 vom: 16. Juni, Seite 861-871 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:29 year:2018 number:5 day:16 month:06 pages:861-871 https://doi.org/10.1007/s00138-018-0942-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 29 2018 5 16 06 861-871 |
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10.1007/s00138-018-0942-y doi (DE-627)OLC2074632053 (DE-He213)s00138-018-0942-y-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Lei, Zhengchao verfasserin aut Scene text recognition using residual convolutional recurrent neural network 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural network (CRNN) is realized by combining convolutional neural network (CNN) with recurrent neural network (RNN). The CNN part extracts features and the RNN part encodes and decodes feature sequences. In order to improve the accuracy rate of scene text recognition based on CRNN, we explore different deeper CNN architectures to get feature descriptors and analyze the corresponding text recognition results. Specifically, VGG and ResNet are introduced to train these different deep models and obtain the encoding information of images. The experimental results on public datasets demonstrate the effectiveness of our method. Residual convolutional recurrent neural network Scene text recognition Convolutional neural network Recurrent neural network Residual network Zhao, Sanyuan aut Song, Hongmei aut Shen, Jianbing aut Enthalten in Machine vision and applications Springer Berlin Heidelberg, 1988 29(2018), 5 vom: 16. Juni, Seite 861-871 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:29 year:2018 number:5 day:16 month:06 pages:861-871 https://doi.org/10.1007/s00138-018-0942-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 29 2018 5 16 06 861-871 |
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10.1007/s00138-018-0942-y doi (DE-627)OLC2074632053 (DE-He213)s00138-018-0942-y-p DE-627 ger DE-627 rakwb eng 004 VZ 11 ssgn Lei, Zhengchao verfasserin aut Scene text recognition using residual convolutional recurrent neural network 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural network (CRNN) is realized by combining convolutional neural network (CNN) with recurrent neural network (RNN). The CNN part extracts features and the RNN part encodes and decodes feature sequences. In order to improve the accuracy rate of scene text recognition based on CRNN, we explore different deeper CNN architectures to get feature descriptors and analyze the corresponding text recognition results. Specifically, VGG and ResNet are introduced to train these different deep models and obtain the encoding information of images. The experimental results on public datasets demonstrate the effectiveness of our method. Residual convolutional recurrent neural network Scene text recognition Convolutional neural network Recurrent neural network Residual network Zhao, Sanyuan aut Song, Hongmei aut Shen, Jianbing aut Enthalten in Machine vision and applications Springer Berlin Heidelberg, 1988 29(2018), 5 vom: 16. Juni, Seite 861-871 (DE-627)129248843 (DE-600)59385-0 (DE-576)017944139 0932-8092 nnns volume:29 year:2018 number:5 day:16 month:06 pages:861-871 https://doi.org/10.1007/s00138-018-0942-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 29 2018 5 16 06 861-871 |
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Abstract Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural network (CRNN) is realized by combining convolutional neural network (CNN) with recurrent neural network (RNN). The CNN part extracts features and the RNN part encodes and decodes feature sequences. In order to improve the accuracy rate of scene text recognition based on CRNN, we explore different deeper CNN architectures to get feature descriptors and analyze the corresponding text recognition results. Specifically, VGG and ResNet are introduced to train these different deep models and obtain the encoding information of images. The experimental results on public datasets demonstrate the effectiveness of our method. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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Abstract Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural network (CRNN) is realized by combining convolutional neural network (CNN) with recurrent neural network (RNN). The CNN part extracts features and the RNN part encodes and decodes feature sequences. In order to improve the accuracy rate of scene text recognition based on CRNN, we explore different deeper CNN architectures to get feature descriptors and analyze the corresponding text recognition results. Specifically, VGG and ResNet are introduced to train these different deep models and obtain the encoding information of images. The experimental results on public datasets demonstrate the effectiveness of our method. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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Abstract Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural network (CRNN) is realized by combining convolutional neural network (CNN) with recurrent neural network (RNN). The CNN part extracts features and the RNN part encodes and decodes feature sequences. In order to improve the accuracy rate of scene text recognition based on CRNN, we explore different deeper CNN architectures to get feature descriptors and analyze the corresponding text recognition results. Specifically, VGG and ResNet are introduced to train these different deep models and obtain the encoding information of images. The experimental results on public datasets demonstrate the effectiveness of our method. © Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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10.1007/s00138-018-0942-y |
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2024-07-03T22:54:42.375Z |
| _version_ |
1803600299167842304 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2074632053</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230401063324.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00138-018-0942-y</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2074632053</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00138-018-0942-y-p</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="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">11</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lei, Zhengchao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Scene text recognition using residual convolutional recurrent neural network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag GmbH Germany, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Text is a significant tool for human communication, and text recognition in scene images becomes more and more important. In this paper, we propose a residual convolutional recurrent neural network for solving the task of scene text recognition. The general convolutional recurrent neural network (CRNN) is realized by combining convolutional neural network (CNN) with recurrent neural network (RNN). The CNN part extracts features and the RNN part encodes and decodes feature sequences. In order to improve the accuracy rate of scene text recognition based on CRNN, we explore different deeper CNN architectures to get feature descriptors and analyze the corresponding text recognition results. Specifically, VGG and ResNet are introduced to train these different deep models and obtain the encoding information of images. 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7.3983088 |