concord
发表于 2025-3-21 19:17:03
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HALL
发表于 2025-3-21 23:55:22
Friedrich Luithlendetection in both business documents and technical articles. By training with .-13., we demonstrate the feasibility of a single solution that can report superior performance compared to the equivalent ones trained with a much larger amount of data, for table detection. We hope that our dataset helps
暗讽
发表于 2025-3-22 02:03:18
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热情赞扬
发表于 2025-3-22 07:09:54
Friedrich Luithlenension but also heavy weight writer specific parameter fixation logic. In this manuscript, we propose an efficient and light weight OSV model for resource-constrained mobile devices. Our approach employs dimensionality reduction based on DBSCAN clustering technique and user specific parameter select
使尴尬
发表于 2025-3-22 08:47:43
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宽敞
发表于 2025-3-22 16:40:53
Friedrich Luithlenthe added reference points are small, their positions can be predicted more precisely, leading to higher center-line detection accuracy. Consequently, our DQ-DETR achieves state-of-the-art performance on five public text detection benchmarks, including MLT2017, Total-Text, CTW1500, ArT and DAST1500.
凝乳
发表于 2025-3-22 17:50:53
Friedrich Luithlenly updated using the student model. Finally, it is used to evaluate. We term the framework Incremental Teacher Model. The proposed framework was applied to four architectures of distinct handwriting recognizers. For almost every architecture, the recognizer trained by our method outperforms those tr
维持
发表于 2025-3-22 23:43:05
Friedrich Luithlenford, SCUT, SCUT-EnsText and ICDAR2013 datasets, TPFNet outperforms recent networks on nearly all the metrics. E.g., on Oxford dataset, TPFNet has a PSNR (higher is better) of 44.2 and a text-detection precision (lower is better) of 39.0, compared to MTRNet++’s PSNR of 33.7 and precision of 50.4. Th
的阐明
发表于 2025-3-23 04:00:21
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牵索
发表于 2025-3-23 05:40:13
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