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

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scribe the functional design knowledge. Nowadays, the acquisition of functional units is mainly manual, which is time-consuming and labor-intensive. Functional knowledge integration is an effective way to achieve innovation design, yet the insufficient functional units cannot effectively support the

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c blocks. This paper introduces a new perspective on this task by utilizing global semantic pair relations from both token- and sentence-level language models. This approach addresses the limitations of prior work, which concentrated solely on individual semantic units like sentences. Our model proc

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Within the Box: Captives of Our Own Mind, understanding (VDU) tasks. Currently, there is a reliance on large document foundation models that offer advanced capabilities but come with a heavy computational burden. In this paper, we propose a multimodal early exit (EE) model design that incorporates various training strategies, exit layer ty

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ncing performance in relation extraction tasks by leveraging dependency trees. However, noise in automatically generated dependency trees poses a challenge to using syntactic dependency information effectively. In this paper, we propose an Adaptive Graph Attention Network model based on Dependency T

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Abduct

A Hybrid Approach for Document Layout Analysis in Document Imagesd PubTables benchmarks show that our approach outperforms current state-of-the-art methods. It achieves an average precision of . on PubLayNet, . on DocLayNet, and . on PubTables, demonstrating its superior performance in layout analysis. These advancements not only enhance the conversion of documen

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DLAFormer: An End-to-End Transformer For Document Layout Analysisiple tasks concurrently. Additionally, we introduce a novel set of . to enhance the physical meaning of content queries in DETR. Moreover, we adopt a coarse-to-fine strategy to accurately identify graphical page objects. Experimental results demonstrate that our proposed DLAFormer outperforms previo

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A Region-Based Approach for Layout Analysis of Music Score Images in Scarce Data Scenariosnimal labeled data necessary for an effective model and demonstrated that our method could achieve a performance comparable with the state-of-the-art with just 8 to 32 labeled samples. The implications of our research extend beyond improving LA, providing a scalable and practical solution for digiti

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Doc-DINO: A Transformer Model for Complex Logical Document Layout Analysisncludes convolutional attention and convolutional feedforward networks to better capture relationships between inputs and enhance the model’s expressive power. The model achieves a mean Average Precision (mAP) of 65.7 on the complex document layout analysis dataset M6Doc and 64.2 on SCUT-CAB, settin

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