Titlebook: Open-Set Text Recognition; Concepts, Framework, Xu-Cheng Yin,Chun Yang,Chang Liu Book 2024 The Editor(s) (if applicable) and The Author(s),

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Open-Set Text Recognition: Case-Studies,ce its motivation, overall design, implementation of individual modules, and training techniques including regularization terms and practical label sampling tricks. This chapter also includes a summarization of the performances of the two models, together with some additional results from the released code.

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2191-5768 , which helps readers to build solutions that strive for an .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

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Background,ses on general ideas instead of implementations. Besides, the chapter also lays the background of conventional OCR methods, which can be categorized into three main frameworks: Word Level Prediction, Feature Aggregation, and Label Aggregation. Finally, we also introduce some existing studies beyond close-set text recognition before the OSTR task.

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Discussions and Future Directions,nderstanding enhancement. In the end, this chapter discusses four main trends in the development of open-set text recognition techniques: (a) Cross-language text recognition technology, (b) Character fine-grained analysis techniques, (c) New character induction discovery techniques, and (d) Incremental language model evolution techniques.

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Book 2024pability 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 th

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