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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...

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Dades bibliogràfiques
Autors principals: Yin, Xu-Cheng (Autor), Yang, Chun (Autor), Liu, Chang (Autor)
Autor corporatiu: SpringerLink (Online service)
Format: Electrònic eBook
Idioma:anglès
Publicat: Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Edició:1st ed. 2024.
Col·lecció:SpringerBriefs in Computer Science,
Matèries:
Image processing > Digital techniques.
Computer vision.
Machine learning.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Machine Learning.
Computer Vision.
Accés en línia:https://doi.org/10.1007/978-981-97-0361-6
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Taula de continguts:
  • 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. .

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