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Titlebook: Document Analysis and Recognition - ICDAR 2023; 17th International C Gernot A. Fink,Rajiv Jain,Richard Zanibbi Conference proceedings 2023

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31#
發(fā)表于 2025-3-26 22:36:00 | 只看該作者
Improving Information Extraction from?Semi-structured Documents Using Attention Based Semi-variation. We tested the architecture on two artificially generated datasets: Gen-Invoices and Gen-Payslips and one real dataset: receipts issued from the SROIE ICDAR 2019 competition. The latter data set yielded an important F1 score of 97.94%, placing our system among the best systems on this dataset.
32#
發(fā)表于 2025-3-27 03:04:00 | 只看該作者
Language Independent Neuro-Symbolic Semantic Parsing for?Form Understanding layout information to facilitate easy transfer across languages. To further improve the performance of ., and achieve isomorphism between entity-relation graphs and word-relation graphs, we use integer linear programming (ILP) based inference. Code is publicly available at ..
33#
發(fā)表于 2025-3-27 06:28:45 | 只看該作者
DocILE Benchmark for?Document Information Localization and?Extraction and DETR-based Table Transformer; applied to both tasks of the DocILE benchmark, with results shared in this paper, offering a quick starting point for future work. The dataset, baselines and supplementary material are available at ..
34#
發(fā)表于 2025-3-27 13:15:27 | 只看該作者
Information Extraction from?Documents: Question Answering Vs Token Classification in?Real-World Setud on Few-Shot Learning and finally Zero-Shot Learning..Our research showed that when dealing with clean and relatively short entities, it is still best to use token classification-based approach, while the QA approach could be a good alternative for noisy environment or long entities use-cases.
35#
發(fā)表于 2025-3-27 17:00:25 | 只看該作者
36#
發(fā)表于 2025-3-27 20:14:10 | 只看該作者
37#
發(fā)表于 2025-3-28 01:18:13 | 只看該作者
Decoupling Visual-Semantic Features Learning with?Dual Masked Autoencoder for?Self-Supervised Scene on this idea, we first propose a siamese network that aligns dual features with each other, then we explore the dual distillation with a co-teacher framework. Our proposed method shows the effectiveness of self-supervised scene text recognition with state-of-the-art performances on most benchmarks.
38#
發(fā)表于 2025-3-28 02:28:46 | 只看該作者
39#
發(fā)表于 2025-3-28 06:19:17 | 只看該作者
Ernestine Wohlfart,Manfred Zaumseilfied as replicable using the similar dataset under certain IoU values. No paper is identified as replicable using the new dataset. We offer observations on the causes of irreproducibility and irreplicability. All code and data are available on Codeocean at ..
40#
發(fā)表于 2025-3-28 12:52:07 | 只看該作者
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