Open-Set Text Recognition Concepts, Framework, and Algorithms

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, whi...

Full description

Bibliographic Details
Main Authors: Yin, Xu-Cheng, Yang, Chun (Author), Liu, Chang (Author)
Format: eBook
Language:English
Published: Singapore Springer Nature Singapore 2024, 2024
Edition:1st ed. 2024
Series:SpringerBriefs in Computer Science
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
LEADER 03197nmm a2200349 u 4500
001 EB002203188
003 EBX01000000000000001340389
005 00000000000000.0
007 cr|||||||||||||||||||||
008 240502 ||| eng
020 |a 9789819703616 
100 1 |a Yin, Xu-Cheng 
245 0 0 |a Open-Set Text Recognition  |h Elektronische Ressource  |b Concepts, Framework, and Algorithms  |c by Xu-Cheng Yin, Chun Yang, Chang Liu 
250 |a 1st ed. 2024 
260 |a Singapore  |b Springer Nature Singapore  |c 2024, 2024 
300 |a XIII, 121 p. 38 illus., 36 illus. in color  |b online resource 
505 0 |a 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. 
653 |a Machine learning 
653 |a Image processing / Digital techniques 
653 |a Machine Learning 
653 |a Computer vision 
653 |a Computer Vision 
653 |a Computer Imaging, Vision, Pattern Recognition and Graphics 
700 1 |a Yang, Chun  |e [author] 
700 1 |a Liu, Chang  |e [author] 
041 0 7 |a eng  |2 ISO 639-2 
989 |b Springer  |a Springer eBooks 2005- 
490 0 |a SpringerBriefs in Computer Science 
028 5 0 |a 10.1007/978-981-97-0361-6 
856 4 0 |u https://doi.org/10.1007/978-981-97-0361-6?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 006 
520 |a 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