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Document Analysis and Recognition - ICDAR 2024 18th International Conference, Athens, Greece, August 30 – September 4, 2024, Proceedings, Part III /

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...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Barney Smith, Elisa H. (Editor), Liwicki, Marcus (Editor), Peng, Liangrui (Editor)
Format: Electronic eBook
Language:English
Published: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Edition:1st ed. 2024.
Series:Lecture Notes in Computer Science, 14806
Subjects:
Image processing > Digital techniques.
Computer vision.
Computer engineering.
Computer networks .
Machine learning.
Natural language processing (Computer science).
Social sciences > Data processing.
Education > Data processing.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Computer Engineering and Networks.
Machine Learning.
Natural Language Processing (NLP).
Computer Application in Social and Behavioral Sciences.
Computers and Education.
Online Access:https://doi.org/10.1007/978-3-031-70543-4
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Summary: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. .
Physical Description:XVII, 412 p. 200 illus., 129 illus. in color. online resource.
ISBN:9783031705434
ISSN:1611-3349 ;
DOI:10.1007/978-3-031-70543-4

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