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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 04:42:00 | 只看該作者
22#
發(fā)表于 2025-3-25 08:25:02 | 只看該作者
978-3-031-41684-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
23#
發(fā)表于 2025-3-25 13:24:16 | 只看該作者
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發(fā)表于 2025-3-25 21:20:49 | 只看該作者
26#
發(fā)表于 2025-3-26 02:11:35 | 只看該作者
Translating Statistics to Make Decisionsarameterization, which grows cubically with respect to the hypercomplex dimension. We attain good word and character error rate at only a small fraction of the memory footprint of non-hypercomplex models as well as previous non-shared operation hypercomplex ones (up to . size reduction).
27#
發(fā)表于 2025-3-26 05:11:21 | 只看該作者
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發(fā)表于 2025-3-26 09:47:26 | 只看該作者
29#
發(fā)表于 2025-3-26 13:27:55 | 只看該作者
What a difference gender makes !to add those samples with a high confidence of correctness to the training set. Experimental results on IAM benchmark task show that OCR model trained with augmented DDPM-synthesized training samples can achieve about . relative word error rate reduction compared with the one trained on real data only.
30#
發(fā)表于 2025-3-26 16:52:14 | 只看該作者
Paola Gentile,María Luisa Rodríguez Mu?ozd loss function for improving non-character resistance, and weakly supervised learning on both character and string samples for improving recognition performance. Experimental results on the CASIA-OLHWDB and ICDAR2013-Online datasets show that the proposed method can achieve promising recognition performance without training data augmentation.
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