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Titlebook: Document Analysis and Recognition – ICDAR 2021; 16th International C Josep Lladós,Daniel Lopresti,Seiichi Uchida Conference proceedings 202

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發(fā)表于 2025-3-23 11:50:41 | 只看該作者
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發(fā)表于 2025-3-24 10:29:56 | 只看該作者
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignmentning mechanism in both the local and global feature maps. It allows the predicted boundaries of bounding boxes to break through the limitation of original proposals. A pyramid mask re-scoring module is then integrated to compromise the local and global information and refine the predicted boundaries
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發(fā)表于 2025-3-24 11:57:39 | 只看該作者
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發(fā)表于 2025-3-24 17:17:17 | 只看該作者
: A Unified Toolkit for Deep Learning Based Document Image Analysisd applications. The core . library comes with a set of simple and intuitive interfaces for applying and customizing DL models for layout detection, character recognition, and many other document processing tasks. To promote extensibility, . also incorporates a community platform for sharing both pre
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發(fā)表于 2025-3-24 22:00:28 | 只看該作者
Understanding and Mitigating the Impact of Model Compression for Document Image Classificationression algorithms can be successfully applied for the task of document image classification. We further analyze the impact of model compression on network outputs and highlight the discrepancy that arises during the compression process. Building on recent findings in this direction, we employ a pri
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發(fā)表于 2025-3-25 02:41:59 | 只看該作者
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