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Titlebook: Document Analysis and Recognition – ICDAR 2023 Workshops; San José, CA, USA, A Mickael Coustaty,Alicia Fornés Conference proceedings 2023 T

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書目名稱Document Analysis and Recognition – ICDAR 2023 Workshops
副標(biāo)題San José, CA, USA, A
編輯Mickael Coustaty,Alicia Fornés
視頻videohttp://file.papertrans.cn/283/282318/282318.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Document Analysis and Recognition – ICDAR 2023 Workshops; San José, CA, USA, A Mickael Coustaty,Alicia Fornés Conference proceedings 2023 T
描述.This two-volume set LNCS 14193-14194 constitutes the proceedings of International Workshops co-located with the 17th International Conference on Document Analysis and Recognition, ICDAR 2023, held in San José, CA, USA, during August 21–26, 2023...The total of 43 regular papers presented in this book were carefully selected from 60 submissions.?..Part I contains 22 regular papers that stem from the following workshops:..ICDAR 2023 Workshop on Computational Paleography (IWCP);..ICDAR 2023 Workshop on Camera-Based Document Analysis and Recognition (CBDAR); ..ICDAR 2023 International Workshop on Graphics Recognition (GREC); ..ICDAR 2023 Workshop on Automatically Domain-Adapted and Personalized Document Analysis (ADAPDA);..Part II contains 21 regular papers that stem from the following workshops:.ICDAR 2023 Workshop on Machine Vision and NLP for Document Analysis (VINALDO);..ICDAR 2023 International Workshop on MachineLearning (WML)...?.
出版日期Conference proceedings 2023
關(guān)鍵詞Document Image Analysis and Recognition; Natural Language Processing; Computational Paleography; Digita
版次1
doihttps://doi.org/10.1007/978-3-031-41498-5
isbn_softcover978-3-031-41497-8
isbn_ebook978-3-031-41498-5Series 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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The Adaptability of?a?Transformer-Based OCR Model for?Historical Documentspect for libraries and archives that must digitise various sources. The digitisation process cannot rely solely on manual transcription due to the complexity and diversity of historical materials. Therefore, text recognition models must be able to adapt to various printed texts and manuscripts, espe
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MuraNet: Multi-task Floor Plan Recognition with?Relation Attentions can result in ineffective utilization of relevant information when there are multiple tasks present simultaneously. To address this challenge, we introduce MuraNet, an attention-based multi-task model for segmentation and detection tasks in floor plan data. In MuraNet, we adopt a unified encoder c
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