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Titlebook: Document Analysis and Recognition - ICDAR 2023; 17th International C Gernot A. Fink,Rajiv Jain,Richard Zanibbi Conference proceedings 2023

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書目名稱Document Analysis and Recognition - ICDAR 2023
副標(biāo)題17th International C
編輯Gernot A. Fink,Rajiv Jain,Richard Zanibbi
視頻videohttp://file.papertrans.cn/283/282308/282308.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Document Analysis and Recognition - ICDAR 2023; 17th International C Gernot A. Fink,Rajiv Jain,Richard Zanibbi Conference proceedings 2023
描述.This six-volume set of LNCS 14187, 14188, 14189, 14190, 14191 and 14192 constitutes the refereed proceedings of the 17.th.?International Conference on Document Analysis and Recognition, ICDAR 2021, held in San José, CA, USA, in August 2023.?The 53 full papers were carefully reviewed and selected from 316 submissions, and are presented with 101 poster presentations...The papers are organized into the following topical sections: Graphics Recognition, Frontiers in Handwriting Recognition, Document Analysis and Recognition..
出版日期Conference proceedings 2023
關(guān)鍵詞Document Analysis Systems; artificial intelligence; Document Layout and Parsing; Document Information E
版次1
doihttps://doi.org/10.1007/978-3-031-41731-3
isbn_softcover978-3-031-41730-6
isbn_ebook978-3-031-41731-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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TPFNet: A Novel ,ext In-,ainting Trans,ormer for?Text Removalford, SCUT, SCUT-EnsText and ICDAR2013 datasets, TPFNet outperforms recent networks on nearly all the metrics. E.g., on Oxford dataset, TPFNet has a PSNR (higher is better) of 44.2 and a text-detection precision (lower is better) of 39.0, compared to MTRNet++’s PSNR of 33.7 and precision of 50.4. Th
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Advances in Global Change Research novel fine-tuning step to update the parameters by adding adaptive but random perturbations, which is quite different from the traditional gradient descent approach. Competitive results on ICDAR (2013, 2015, 2017), CTW-1500, and SynthText benchmarks further demonstrate the effectiveness of TDAE.
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