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Titlebook: Document Analysis Systems; 16th IAPR Internatio Giorgos Sfikas,George Retsinas Conference proceedings 2024 The Editor(s) (if applicable) an

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樓主: collude
31#
發(fā)表于 2025-3-27 00:14:17 | 只看該作者
Conference proceedings 2024, during August 30-31, 2024..The 27? full papers presented were carefully reviewed and selected from 43 submissions addressing topics like: document analysis and understanding; retrieval and VQA; layout analysis; document classification; OCR correction and NLP; recognition systems; and historical do
32#
發(fā)表于 2025-3-27 01:49:01 | 只看該作者
33#
發(fā)表于 2025-3-27 08:39:12 | 只看該作者
34#
發(fā)表于 2025-3-27 10:13:17 | 只看該作者
Enhanced Bank Check Security: Introducing a?Novel Dataset and?Transformer-Based Approach for?Detectio the coexistence of signatures with other textual and graphical elements on real-world documents. Verification systems must first detect the signature and then validate its authenticity, a dual challenge often overlooked by current datasets and methodologies focusing only on verification. To addres
35#
發(fā)表于 2025-3-27 16:27:41 | 只看該作者
36#
發(fā)表于 2025-3-27 17:59:17 | 只看該作者
Instruction Makes a?Differencee-Vision (LV) models for document analysis and predictions on document images, respectively. Usually, deep neural networks for the DocVQA task are trained on datasets lacking instructions. We show that using instruction-following datasets improves performance. We compare performance across document-
37#
發(fā)表于 2025-3-27 23:08:22 | 只看該作者
38#
發(fā)表于 2025-3-28 04:48:52 | 只看該作者
39#
發(fā)表于 2025-3-28 07:47:42 | 只看該作者
LD-DOC: Light-Weight Domain-Adaptive Document Layout Analysiscument regions under limited data conditions. The LD-DOC model effectively utilizes information from various scale visual features, enhancing its adaptability to feature distributions in scenarios with limited data and thereby improving the accuracy of document region partitioning. Specifically, our
40#
發(fā)表于 2025-3-28 14:04:21 | 只看該作者
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