9789819703609 - open-set text recognition: concepts, framework, and algorithms di yin, xu-cheng; yang, chun; liu, chang (12 risultati)
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Editore: Springer 2024
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Condizione: New. 2024th edition NO-PA16APR2015-KAP.
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Paperback. Condizione: Brand New. 134 pages. 9.25x6.10x0.29 inches. In Stock.
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Serie: SpringerBriefs in Computer Science, Libro 54 di 83. Libro 54 di 83 - SpringerBriefs in Computer Science
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Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - In real-world applications, new data, patterns, and categories that were not covered by the training data can frequently emerge, necessitating the capability to detect and adapt to novel characters incrementally. Researchers refer to these challen…ges as the Open-Set Text Recognition (OSTR) task, which has, in recent years, emerged as one of the prominent issues in the field of text recognition. This book begins by providing an introduction to the background of the OSTR task, covering essential aspects such as open-set identification and recognition, conventional OCR methods, and their applications. Subsequently, the concept and definition of the OSTR task are presented encompassing its objectives, use cases, performance metrics, datasets, and protocols. A general framework for OSTR is then detailed, composed of four key components: The Aligned Represented Space, the Label-to-Representation Mapping, the Sample-to-Representation Mapping, and the Open-set Predictor. In addition,possible implementations of each module withinthe framework are discussed. Following this, two specific open-set text recognition methods, OSOCR and OpenCCD, are introduced. The book concludes by delving into applications and future directions of Open-set text recognition tasks.This book presents a comprehensive overview of the open-set text recognition task, including concepts, framework, and algorithms. It is suitable for graduated students and young researchers who are majoring in pattern recognition and computer science, especially interdisciplinary research.
Lingua: Inglese
Editore: Springer 2024
Serie: SpringerBriefs in Computer Science, Libro 54 di 83. Libro 54 di 83 - SpringerBriefs in Computer Science
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Taschenbuch. Condizione: Neu. Open-Set Text Recognition | Concepts, Framework, and Algorithms | Xu-Cheng Yin (u. a.) | Taschenbuch | xiii | Englisch | 2024 | Springer | EAN 9789819703609 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbi…eter: preigu.
Lingua: Inglese
Editore: Springer 2024
Serie: SpringerBriefs in Computer Science, Libro 54 di 83. Libro 54 di 83 - SpringerBriefs in Computer Science
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Condizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | In real-world applications, new data, patterns, and categories that were not covered by the training data can frequently emerge, necessitating the capability to detect and adapt to novel characters incrementally. Researchers refer to these… challenges as the Open-Set Text Recognition (OSTR) task, which has, in recent years, emerged as one of the prominent issues in the field of text recognition. This book begins by providing an introduction to the background of the OSTR task, covering essential aspects such as open-set identification and recognition, conventional OCR methods, and their applications. Subsequently, the concept and definition of the OSTR task are presented encompassing its objectives, use cases, performance metrics, datasets, and protocols. A general framework for OSTR is then detailed, composed of four key components: The Aligned Represented Space, the Label-to-Representation Mapping, the Sample-to-Representation Mapping, and the Open-set Predictor. In addition,possible implementations of each module within the framework are discussed. Following this, two specific open-set text recognition methods, OSOCR and OpenCCD, are introduced. The book concludes by delving into applications and future directions of Open-set text recognition tasks.This book presents a comprehensive overview of the open-set text recognition task, including concepts, framework, and algorithms. It is suitable for graduated students and young researchers who are majoring in pattern recognition and computer science, especially interdisciplinary research.
Lingua: Inglese
Editore: Springer 2024
Serie: SpringerBriefs in Computer Science, Libro 54 di 83. Libro 54 di 83 - SpringerBriefs in Computer Science
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Editore: Springer Nature Singapore, Springer Nature Singapore Apr 2024 2024
Serie: SpringerBriefs in Computer Science, Libro 54 di 83. Libro 54 di 83 - SpringerBriefs in Computer Science
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In real-world applications, new data, patterns, and categories that were not covered by the training data can frequently emerge, necessitating the capability to detect and adapt to novel characters incrementally. Researchers refer…to these challenges as the Open-Set Text Recognition (OSTR) task, which has, in recent years, emerged as one of the prominent issues in the field of text recognition. This book begins by providing an introduction to the background of the OSTR task, covering essential aspects such as open-set identification and recognition, conventional OCR methods, and their applications. Subsequently, the concept and definition of the OSTR task are presented encompassing its objectives, use cases, performance metrics, datasets, and protocols. A general framework for OSTR is then detailed, composed of four key components: The Aligned Represented Space, the Label-to-Representation Mapping, the Sample-to-Representation Mapping, and the Open-set Predictor. In addition,possible implementations of each module withinthe framework are discussed. Following this, two specific open-set text recognition methods, OSOCR and OpenCCD, are introduced. The book concludes by delving into applications and future directions of Open-set text recognition tasks.This book presents a comprehensive overview of the open-set text recognition task, including concepts, framework, and algorithms. It is suitable for graduated students and young researchers who are majoring in pattern recognition and computer science, especially interdisciplinary research. 136 pp. Englisch.
Lingua: Inglese
Editore: Springer 2024
Serie: SpringerBriefs in Computer Science, Libro 54 di 83. Libro 54 di 83 - SpringerBriefs in Computer Science
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Editore: Springer, Berlin|Springer Nature Singapore|National Natural Science Foundation of China|National Science Fund for Distinguished Young Scholars|Springer 2024
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In real-world applications, new data, patterns, and categories that were not covered by the training data can frequently emerge, necessitating the capability to detect and adapt to novel characters incrementally. Res…earchers refer to these challenges as .
Lingua: Inglese
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Serie: SpringerBriefs in Computer Science, Libro 54 di 83. Libro 54 di 83 - SpringerBriefs in Computer Science
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In real-world applications, new data, patterns, and categories that were not covered by the training data can frequently emerge, necessitating the capability to detect and adapt to novel characters incrementally. Researchers refer to t…hese challenges as the Open-Set Text Recognition (OSTR) task, which has, in recent years, emerged as one of the prominent issues in the field of text recognition. This book begins by providing an introduction to the background of the OSTR task, covering essential aspects such as open-set identification and recognition, conventional OCR methods, and their applications. Subsequently, the concept and definition of the OSTR task are presented encompassing its objectives, use cases, performance metrics, datasets, and protocols. A general framework for OSTR is then detailed, composed of four key components: The Aligned Represented Space, the Label-to-Representation Mapping, the Sample-to-Representation Mapping, and the Open-set Predictor. In addition,possible implementations of each module within the framework are discussed. Following this, two specific open-set text recognition methods, OSOCR and OpenCCD, are introduced. The book concludes by delving into applications and future directions of Open-set text recognition tasks.This book presents a comprehensive overview of the open-set text recognition task, including concepts, framework, and algorithms. It is suitable for graduated students and young researchers who are majoring in pattern recognition and computer science, especially interdisciplinary research.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 136 pp. Englisch.
