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Titlebook: Document Analysis and Recognition - ICDAR 2024; 18th International C Elisa H. Barney Smith,Marcus Liwicki,Liangrui Peng Conference proceedi

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發(fā)表于 2025-3-21 16:29:37 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Document Analysis and Recognition - ICDAR 2024
副標(biāo)題18th International C
編輯Elisa H. Barney Smith,Marcus Liwicki,Liangrui Peng
視頻videohttp://file.papertrans.cn/285/284814/284814.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Document Analysis and Recognition - ICDAR 2024; 18th International C Elisa H. Barney Smith,Marcus Liwicki,Liangrui Peng Conference proceedi
描述.This six-volume set LNCS 14804-14809 constitutes the proceedings of the 18th International Conference on Document Analysis and Recognition, ICDAR 2024, held in Athens, Greece, during August 30–September 4, 2024..The total of 144 full papers presented in these proceedings were carefully selected from 263 submissions..The papers reflect topics such as: document image processing; physical and logical layout analysis; text and symbol recognition; handwriting recognition; document analysis systems; document classification; indexing and retrieval of documents; document synthesis; extracting document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more...?.
出版日期Conference proceedings 2024
關(guān)鍵詞Document Analysis Systems; Handwriting Recognition; Scene Text Detection and Recognition; Document Imag
版次1
doihttps://doi.org/10.1007/978-3-031-70549-6
isbn_softcover978-3-031-70548-9
isbn_ebook978-3-031-70549-6Series 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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發(fā)表于 2025-3-21 22:36:15 | 只看該作者
ICAL: Implicit Character-Aided Learning for?Enhanced Handwritten Mathematical Expression Recognition difficult to model global information in .. Therefore, this paper introduces a novel approach, Implicit Character-Aided Learning (ICAL), to mine the global expression information and enhance handwritten mathematical expression recognition. Specifically, we propose the Implicit Character Constructio
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A Real-Time Scene Uyghur Text Detection Network Based on?Feature Complementatione task of detecting Uyghur in natural scene images is of great significance to the intelligent information technology industry and economic construction. In fact, current text detection applications in complex scenes have large models, and sluggish detection rates, and are challenging to implement o
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MOoSE: Multi-Orientation Sharing Experts for?Open-Set Scene Text Recognitionn field. However, the current open-set text recognition solutions only focuses on horizontal text, which fail to model the real-life challenges posed by the variety of writing directions in real-world scene text. Multi-orientation text recognition, in general, faces challenges from diverse image asp
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More and?Less: Enhancing Abundance and?Refining Redundancy for?Text-Prior-Guided Scene Text Image Suleverage text recognizer for prior information, achieving superior performance via a novel strategy. However, we observe abundant erroneous prior information from the low-resolution (LR) text images processed by the text recognizer, which can mislead text reconstruction when fused with image feature
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