標題: 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), [打印本頁] 作者: 快樂 時間: 2025-3-21 16:29
書目名稱Open-Set Text Recognition影響因子(影響力)
書目名稱Open-Set Text Recognition影響因子(影響力)學(xué)科排名
書目名稱Open-Set Text Recognition網(wǎng)絡(luò)公開度
書目名稱Open-Set Text Recognition網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Open-Set Text Recognition被引頻次
書目名稱Open-Set Text Recognition被引頻次學(xué)科排名
書目名稱Open-Set Text Recognition年度引用
書目名稱Open-Set Text Recognition年度引用學(xué)科排名
書目名稱Open-Set Text Recognition讀者反饋
書目名稱Open-Set Text Recognition讀者反饋學(xué)科排名
作者: Landlocked 時間: 2025-3-21 22:35 作者: 狂怒 時間: 2025-3-22 01:37
Xu-Cheng Yin,Chun Yang,Chang Liurohibition of exploitative abuses of a dominant position would tend to instantiate various egalitarian conceptions of the moral good and probably would not decrease economic efficiency if it did not deter firms from making decisions that would cause them to occupy a dominant position, the test’s ten作者: 進入 時間: 2025-3-22 07:53 作者: 緊張過度 時間: 2025-3-22 10:29 作者: 鋪子 時間: 2025-3-22 13:09
2191-5768 n,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 int978-981-97-0360-9978-981-97-0361-6Series ISSN 2191-5768 Series E-ISSN 2191-5776 作者: 車床 時間: 2025-3-22 19:37
Open-Set Text Recognition: Concept, Dataset, Protocol, and Framework,tion to the functionality. More implementations will be introduced in detail in later chapters. Open-Set Text Recognition: Concept, Dataset, and Protocol In addition, we explain the backward compatibility by examples, i.e., how the framework also fits zero-shot and close-set text recognition methods作者: endure 時間: 2025-3-23 00:57 作者: 連累 時間: 2025-3-23 04:15 作者: Scintigraphy 時間: 2025-3-23 05:38 作者: 翅膀拍動 時間: 2025-3-23 10:57
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.作者: Phonophobia 時間: 2025-3-23 14:13
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作者: 寒冷 時間: 2025-3-23 22:05
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.作者: ABASH 時間: 2025-3-24 01:08 作者: Interim 時間: 2025-3-24 05:51
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.作者: defibrillator 時間: 2025-3-24 09:20 作者: 合同 時間: 2025-3-24 11:32
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作者: 淺灘 時間: 2025-3-24 16:36 作者: Circumscribe 時間: 2025-3-24 21:06 作者: CRUMB 時間: 2025-3-25 00:08 作者: lethargy 時間: 2025-3-25 04:29
Open-Set Text Recognition978-981-97-0361-6Series ISSN 2191-5768 Series E-ISSN 2191-5776 作者: Nonthreatening 時間: 2025-3-25 11:19 作者: arousal 時間: 2025-3-25 12:25 作者: 引起痛苦 時間: 2025-3-25 19:35 作者: 懶惰人民 時間: 2025-3-25 23:25 作者: 思想 時間: 2025-3-26 02:55
Xu-Cheng Yin,Chun Yang,Chang Liuts (Chalopin & Gon?alves, SODA 2009), .-shapes (Gon?alves?.., SODA 2018). For general graphs, however, even deciding whether such representations exist is often .-hard. We consider apex graphs, ...., graphs that can be made planar by removing one vertex from them. We show, somewhat surprisingly, tha作者: anus928 時間: 2025-3-26 08:16
Xu-Cheng Yin,Chun Yang,Chang Liuts (Chalopin & Gon?alves, SODA 2009), .-shapes (Gon?alves?.., SODA 2018). For general graphs, however, even deciding whether such representations exist is often .-hard. We consider apex graphs, ...., graphs that can be made planar by removing one vertex from them. We show, somewhat surprisingly, tha作者: Fester 時間: 2025-3-26 09:07 作者: 祝賀 時間: 2025-3-26 13:38
Background,conventional OCR methods, and their applications. First, we introduce the concept of open-set (or open-world). We discuss the different usage of open-set from various research areas and declare the OSTR task concern on either identification or recognition capability, with both seen and novel (abnorm作者: Glossy 時間: 2025-3-26 16:47
Open-Set Text Recognition: Concept, Dataset, Protocol, and Framework,sk and the relation between OSTR and other tasks. Secondly, we narrow down to the specific protocols used to measure model performances on the task. Before reaching the specific protocol, we introduce the commonly used protocols and datasets to lay out the background. Finally, this chapter presents 作者: atopic 時間: 2025-3-26 23:57
Open-Set Text Recognition Implementations(I): Label-to-Representation Mapping, representation space and the label-to-representation mapping module. First, this chapter introduces how characters, or other corresponding granularities, are represented in different methods, i.e., the representation space, where class centers (prototypes) and features extracted from input images r作者: 貧窮地活 時間: 2025-3-27 02:23
Open-Set Text Recognition Implementations(II): Sample-to-Representation Mapping, discussed above (Fig.?.). The contents include three parts: feature extraction, representation aggregation, and context handling. Since most mapping approaches break down into a feature extractor and a sampler module, specifically, the feature extractor maps input images to feature maps, whereas th作者: Trypsin 時間: 2025-3-27 07:30
Open-Set Text Recognition Implementations(III): Open-set Predictor, in question. For each query instance representation extracted from the sample image, the open-set predictor matches it to representation prototype and determines whether the character belongs to a corresponding class or not. If the instance representation successfully matches a representation proto作者: Entirety 時間: 2025-3-27 11:23 作者: NEEDY 時間: 2025-3-27 16:18
Discussions and Future Directions,ations in OSTR tasks, from different perspectives: recognized language, text recognition granularity, the overall technical route, and open-set classifier design and model implementation. Then we discuss the influence of the Multi-modal Large Language Model on the Open-Set Text Recognition (OSTR) ta作者: MORPH 時間: 2025-3-27 18:28
9樓作者: 抱狗不敢前 時間: 2025-3-27 23:41
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