03947nam 22006255 450 991084706880332120250807143306.0981-9703-61-110.1007/978-981-97-0361-6(MiAaPQ)EBC31246368(Au-PeEL)EBL31246368(CKB)31326333400041(MiAaPQ)EBC31266857(Au-PeEL)EBL31266857(DE-He213)978-981-97-0361-6(EXLCZ)993132633340004120240401d2024 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierOpen-Set Text Recognition Concepts, Framework, and Algorithms /by Xu-Cheng Yin, Chun Yang, Chang Liu1st ed. 2024.Singapore :Springer Nature Singapore :Imprint: Springer,2024.1 online resource (130 pages)SpringerBriefs in Computer Science,2191-5776981-9703-60-3 Includes bibliographical references.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. .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.SpringerBriefs in Computer Science,2191-5776Image processingDigital techniquesComputer visionMachine learningComputer Imaging, Vision, Pattern Recognition and GraphicsMachine LearningComputer VisionImage processingDigital techniques.Computer vision.Machine learning.Computer Imaging, Vision, Pattern Recognition and Graphics.Machine Learning.Computer Vision.780Yin Xu-Cheng1735032Yang ChunLiu ChangMiAaPQMiAaPQMiAaPQBOOK9910847068803321Open-Set Text Recognition4255109UNINA