000 04142nam a22005655i 4500
001 978-981-97-0361-6
003 DE-He213
005 20240730171739.0
007 cr nn 008mamaa
008 240401s2024 si | s |||| 0|eng d
020 _a9789819703616
_9978-981-97-0361-6
024 7 _a10.1007/978-981-97-0361-6
_2doi
050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYT
_2thema
082 0 4 _a006
_223
100 1 _aYin, Xu-Cheng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100224
245 1 0 _aOpen-Set Text Recognition
_h[electronic resource] :
_bConcepts, Framework, and Algorithms /
_cby Xu-Cheng Yin, Chun Yang, Chang Liu.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXIII, 121 p. 38 illus., 36 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5776
505 0 _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. .
520 _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.
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_9100227
650 0 _aMachine learning.
_91831
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aMachine Learning.
_91831
650 2 4 _aComputer Vision.
_9100229
700 1 _aYang, Chun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100231
700 1 _aLiu, Chang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100233
710 2 _aSpringerLink (Online service)
_9100235
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819703609
776 0 8 _iPrinted edition:
_z9789819703623
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
_9100237
856 4 0 _uhttps://doi.org/10.1007/978-981-97-0361-6
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cEBK
999 _c87792
_d87792