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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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21#
發(fā)表于 2025-3-25 03:46:34 | 只看該作者
Palgrave Studies in Languages at Wars with their corresponding named entities. We compare our models to state-of-the-art methods on three public databases (IAM, ESPOSALLES, and POPP) and outperform previous performances on all three datasets.
22#
發(fā)表于 2025-3-25 09:34:15 | 只看該作者
Robustness Evaluation of?Transformer-Based Form Field Extractors via?Form Attacksisruption of the neighboring words of field-values(. 10% drop in F1 score). Guided by the analysis, we make recommendations to improve the design of field extractors and the process of data collection. Code will be available at ..
23#
發(fā)表于 2025-3-25 12:12:01 | 只看該作者
Key-Value Information Extraction from?Full Handwritten Pagess with their corresponding named entities. We compare our models to state-of-the-art methods on three public databases (IAM, ESPOSALLES, and POPP) and outperform previous performances on all three datasets.
24#
發(fā)表于 2025-3-25 16:31:06 | 只看該作者
0302-9743 nference on Document Analysis and Recognition, ICDAR 2021, held in San José, CA, USA, in August 2023.?The 53 full papers were carefully reviewed and selected from 316 submissions, and are presented with 101 poster presentations...The papers are organized into the following topical sections: Graphics
25#
發(fā)表于 2025-3-25 21:07:14 | 只看該作者
26#
發(fā)表于 2025-3-26 01:09:57 | 只看該作者
27#
發(fā)表于 2025-3-26 06:13:14 | 只看該作者
28#
發(fā)表于 2025-3-26 10:23:58 | 只看該作者
29#
發(fā)表于 2025-3-26 13:21:49 | 只看該作者
SpaDen: Sparse and?Dense Keypoint Estimation for?Real-World Chart Understanding estimation and the combination of deep layer aggregation and corner pooling approaches. The results of our experiments provide extensive evaluation for the task of real-world chart data extraction. Our Code is publicly available (.).
30#
發(fā)表于 2025-3-26 16:49:42 | 只看該作者
Generalization of?Fine Granular Extractions from?Chartse mentioned shifts in chart element distributions. We demonstrate the generalization capabilities of our models trained on the PlotQA train set by providing chart extraction results on out-of-distribution charts selected from the LeafQA dataset. We achieve an mAP of 90.64% and 92.18% for @0.90 IOU f
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