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

財(cái)產(chǎn)

Conference proceedings 2024ndwriting 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...?.

編輯才信任

0302-9743 ng document semantics; NLP for document understanding; office automation; graphics recognition; human document interaction; document representation modeling and much more...?.978-3-031-70532-8978-3-031-70533-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

Indent

https://doi.org/10.1007/978-3-662-13130-5ds. Experiments using Convolutional Neural Networks showed that using class decomposition significantly improves the classification performance that can be achieved, without causing information loss, as it is the case with other class imbalance data sampling approaches.

Malfunction

https://doi.org/10.1007/978-3-662-10287-9o verify OVD shapes and dynamics with very little supervision, this work opens the way towards the use of massive amounts of unlabeled data to build robust remote identity document verification systems on commodity smartphones. Code is available at ..

BLAND

Heretical

A Multiclass Imbalanced Dataset Classification of?Symbols from?Piping and?Instrumentation Diagramsds. Experiments using Convolutional Neural Networks showed that using class decomposition significantly improves the classification performance that can be achieved, without causing information loss, as it is the case with other class imbalance data sampling approaches.

破譯

欄桿

One-Shot Transformer-Based Framework for?Visually-Rich Document Understandingto the full set of labeled entities in the public SROIE datasets. We have also gathered and annotated the public RVL-CDIP and invoice datasets to showcase the generalization of our OTER models for the EE task across a wide range of document templates, containing both single and multiple-region fields.

鍵琴

Conference proceedings 20244, 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; ha

Fibrillation

Beta-delayed (multi-)particle decay studies, to document resolution variability. Moreover, the few-shot approach allow the model to perform well even for unseen class of documents. Preliminary results on the SIDTD and Findit datasets show good performance of this model for this task.