Scene text detection and recognition: a survey
Abstract Scene text detection and recognition have been given a lot of attention in recent years and have been used in many vision-based applications. In this field, there are various types of challenges, including images with wavy text, images with text rotation and orientation, changing the scale...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Naiemi, Fatemeh [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), 14 vom: 11. März, Seite 20255-20290 |
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| Übergeordnetes Werk: |
volume:81 ; year:2022 ; number:14 ; day:11 ; month:03 ; pages:20255-20290 |
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10.1007/s11042-022-12693-7 |
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| 520 | |a Abstract Scene text detection and recognition have been given a lot of attention in recent years and have been used in many vision-based applications. In this field, there are various types of challenges, including images with wavy text, images with text rotation and orientation, changing the scale and variety of text fonts, noisy images, wild background images, which make the detection and recognition of text from the image more complex and difficult. In this article, we first presented a comprehensive review of recent advances in text detection and recognition and described the advantages and disadvantages. The common datasets were introduced. Then, the recent methods compared together and analyzed the text detection and recognition systems. According to the recent decade studies, one of the most important challenges is curved and vertical text detection in this field. We have expressed approaches for the development of the detection and recognition system. Also, we have described the methods that are robust in the detection and recognition of curved and vertical texts. Finally, we have presented some approaches to develop text detection and recognition systems as the future work. | ||
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Abstract Scene text detection and recognition have been given a lot of attention in recent years and have been used in many vision-based applications. In this field, there are various types of challenges, including images with wavy text, images with text rotation and orientation, changing the scale and variety of text fonts, noisy images, wild background images, which make the detection and recognition of text from the image more complex and difficult. In this article, we first presented a comprehensive review of recent advances in text detection and recognition and described the advantages and disadvantages. The common datasets were introduced. Then, the recent methods compared together and analyzed the text detection and recognition systems. According to the recent decade studies, one of the most important challenges is curved and vertical text detection in this field. We have expressed approaches for the development of the detection and recognition system. Also, we have described the methods that are robust in the detection and recognition of curved and vertical texts. Finally, we have presented some approaches to develop text detection and recognition systems as the future work. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Abstract Scene text detection and recognition have been given a lot of attention in recent years and have been used in many vision-based applications. In this field, there are various types of challenges, including images with wavy text, images with text rotation and orientation, changing the scale and variety of text fonts, noisy images, wild background images, which make the detection and recognition of text from the image more complex and difficult. In this article, we first presented a comprehensive review of recent advances in text detection and recognition and described the advantages and disadvantages. The common datasets were introduced. Then, the recent methods compared together and analyzed the text detection and recognition systems. According to the recent decade studies, one of the most important challenges is curved and vertical text detection in this field. We have expressed approaches for the development of the detection and recognition system. Also, we have described the methods that are robust in the detection and recognition of curved and vertical texts. Finally, we have presented some approaches to develop text detection and recognition systems as the future work. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract Scene text detection and recognition have been given a lot of attention in recent years and have been used in many vision-based applications. In this field, there are various types of challenges, including images with wavy text, images with text rotation and orientation, changing the scale and variety of text fonts, noisy images, wild background images, which make the detection and recognition of text from the image more complex and difficult. In this article, we first presented a comprehensive review of recent advances in text detection and recognition and described the advantages and disadvantages. The common datasets were introduced. Then, the recent methods compared together and analyzed the text detection and recognition systems. According to the recent decade studies, one of the most important challenges is curved and vertical text detection in this field. We have expressed approaches for the development of the detection and recognition system. Also, we have described the methods that are robust in the detection and recognition of curved and vertical texts. Finally, we have presented some approaches to develop text detection and recognition systems as the future work. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Scene text detection and recognition: a survey |
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