Open-Set Text Recognition (Record no. 87792)
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| 000 -LEADER | |
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| fixed length control field | 04142nam a22005655i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-981-97-0361-6 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730171739.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240401s2024 si | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9789819703616 |
| -- | 978-981-97-0361-6 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006 |
| 100 1# - AUTHOR NAME | |
| Author | Yin, Xu-Cheng. |
| 245 10 - TITLE STATEMENT | |
| Title | Open-Set Text Recognition |
| Sub Title | Concepts, Framework, and Algorithms / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XIII, 121 p. 38 illus., 36 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | SpringerBriefs in Computer Science, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Background -- Open-Set Text Recognition: Concept, DataSet, Protocol, and Framework -- Open-Set Text Recognition Implementations(I): Label-to-Representation Mapping -- Open-Set Text Recognition Implementations(II): Sample-to-Representation Mapping -- Open-Set Text Recognition Implementations(III): Open-set Predictor -- Open Set Text Recognition: Case-studies -- Discussions and Future Directions. . |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Digital techniques. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Yang, Chun. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Liu, Chang. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-981-97-0361-6 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Singapore : |
| -- | Springer Nature Singapore : |
| -- | Imprint: Springer, |
| -- | 2024. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| -- | rdacarrier |
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| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Image processing |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer vision. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Imaging, Vision, Pattern Recognition and Graphics. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Vision. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2191-5776 |
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