LEADER 03947nam 22006255 450 001 9910847068803321 005 20250807143306.0 010 $a981-9703-61-1 024 7 $a10.1007/978-981-97-0361-6 035 $a(MiAaPQ)EBC31246368 035 $a(Au-PeEL)EBL31246368 035 $a(CKB)31326333400041 035 $a(MiAaPQ)EBC31266857 035 $a(Au-PeEL)EBL31266857 035 $a(DE-He213)978-981-97-0361-6 035 $a(EXLCZ)9931326333400041 100 $a20240401d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOpen-Set Text Recognition $eConcepts, Framework, and Algorithms /$fby Xu-Cheng Yin, Chun Yang, Chang Liu 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (130 pages) 225 1 $aSpringerBriefs in Computer Science,$x2191-5776 311 08$a981-9703-60-3 320 $aIncludes bibliographical references. 327 $aIntroduction -- 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. . 330 $aIn real-world applications, new data, patterns, and categories that were not covered by the training data can frequently emerge, necessitating the capability to detect and adapt to novel characters incrementally. Researchers refer to these challenges as the Open-Set Text Recognition (OSTR) task, which has, in recent years, emerged as one of the prominent issues in the field of text recognition. This book begins by providing an introduction to the background of the OSTR task, covering essential aspects such as open-set identification and recognition, conventional OCR methods, and their applications. Subsequently, the concept and definition of the OSTR task are presented encompassing its objectives, use cases, performance metrics, datasets, and protocols. A general framework for OSTR is then detailed, composed of four key components: The Aligned Represented Space, the Label-to-Representation Mapping, the Sample-to-Representation Mapping, and the Open-set Predictor. In addition, possible implementations of each module within the framework are discussed. Following this, two specific open-set text recognition methods, OSOCR and OpenCCD, are introduced. The book concludes by delving into applications and future directions of Open-set text recognition tasks. This book presents a comprehensive overview of the open-set text recognition task, including concepts, framework, and algorithms. It is suitable for graduated students and young researchers who are majoring in pattern recognition and computer science, especially interdisciplinary research. 410 0$aSpringerBriefs in Computer Science,$x2191-5776 606 $aImage processing$xDigital techniques 606 $aComputer vision 606 $aMachine learning 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aMachine Learning 606 $aComputer Vision 615 0$aImage processing$xDigital techniques. 615 0$aComputer vision. 615 0$aMachine learning. 615 14$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aMachine Learning. 615 24$aComputer Vision. 676 $a780 700 $aYin$b Xu-Cheng$01735032 702 $aYang$b Chun 702 $aLiu$b Chang 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910847068803321 996 $aOpen-Set Text Recognition$94255109 997 $aUNINA