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Titlebook: Probabilistic Indexing for Information Search and Retrieval in Large Collections of Handwritten Text; Alejandro Héctor Toselli,Joan Puigce

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發(fā)表于 2025-3-21 17:22:29 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Probabilistic Indexing for Information Search and Retrieval in Large Collections of Handwritten Text Images
編輯Alejandro Héctor Toselli,Joan Puigcerver,Enrique V
視頻videohttp://file.papertrans.cn/757/756799/756799.mp4
概述Provides a comprehensive presentation of the "probabilistic indexing" (PrIx) framework.Presents both the mathematical foundations and numerous models for indexing handwritten text in images.Includes e
叢書名稱The Information Retrieval Series
圖書封面Titlebook: Probabilistic Indexing for Information Search and Retrieval in Large Collections of Handwritten Text;  Alejandro Héctor Toselli,Joan Puigce
描述.This book provides a comprehensive presentation of a recently introduced framework, named "probabilistic indexing" (PrIx), for searching text in large collections of document images and other related applications. It fosters the development of new search engines for effective information retrieval from manuscripts which, however, lack the electronic text (transcripts) that would typically be required for such search and retrieval tasks.? ..The book is structured into 11 chapters and three appendices. The first two chapters briefly outline the necessary fundamentals and state of the art in pattern recognition, statistical decision theory, and handwritten text recognition. Chapter 3 presents approaches for indexing (as opposed to “spotting”) each region of a handwritten text image which is likely to contain a word. Next, Chapter 4 describes models adopted for handwritten text in images, namely hidden Markov models, convolutional and recurrent neural networks and language models, and provides full details of weighted finite-state transducer (WFST) concepts and methods, needed in further chapters of the book. Chapter 5 explains the set of techniques and algorithms developed to generat
出版日期Book 2024
關(guān)鍵詞Information Retrieval; Probabilistic Search; Keyword Spotting; Natural Language Processing; Text Analyti
版次1
doihttps://doi.org/10.1007/978-3-031-55389-9
isbn_softcover978-3-031-55391-2
isbn_ebook978-3-031-55389-9Series ISSN 1871-7500 Series E-ISSN 2730-6836
issn_series 1871-7500
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 23:21:02 | 只看該作者
Book 2024amely hidden Markov models, convolutional and recurrent neural networks and language models, and provides full details of weighted finite-state transducer (WFST) concepts and methods, needed in further chapters of the book. Chapter 5 explains the set of techniques and algorithms developed to generat
板凳
發(fā)表于 2025-3-22 03:53:47 | 只看該作者
Alejandro Héctor Toselli,Joan Puigcerver,Enrique Vidalmbiguous grammar and grammars of real programming languages. Our results, compared to the original GLL, show a speedup factor of 10 on the highly-ambiguous grammar, and a speedup factor of 1.5, 1.7, and 5.2 on the grammars of Java, C#, and OCaml, respectively.
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Alejandro Héctor Toselli,Joan Puigcerver,Enrique Vidal the register pressure constraints - either can be minimized while the other one is fixed. After scheduling, register allocation is done cyclically on conventional register sets or on rotating register files. We give an optimal exact model, and another approximative one that generalizes the Ning-Gao
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Alejandro Héctor Toselli,Joan Puigcerver,Enrique Vidalanswering those questions. It first introduces the concept of ., a direct extension of the traditional concept of reuse distance with data references by all co-running threads (or jobs) considered. It then discusses the properties of concurrent reuse distance, revealing the special challenges facing
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