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Titlebook: Document Analysis and Recognition – ICDAR 2021 Workshops; Lausanne, Switzerlan Elisa H. Barney Smith,Umapada Pal Conference proceedings 202

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發(fā)表于 2025-3-26 21:26:32 | 只看該作者
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發(fā)表于 2025-3-27 02:37:21 | 只看該作者
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發(fā)表于 2025-3-27 06:18:05 | 只看該作者
Famous Companies Use More Letters in Logo: A Large-Scale Analysis of Text Area in Logoude the weak positive correlation between the text area ratio and the number of followers of the company. In addition, deep regression and deep ranking methods can catch correlations between the logo images and the number of followers.
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發(fā)表于 2025-3-27 10:36:01 | 只看該作者
Accurate Graphic Symbol Detection in?Ancient Document Digital Reproductionsighting potential symbols to be validated and enriched by the experts, whose decisions are used to improve the detection performance. This paper shows how this task can benefit from feature auto-encoding, showing how detection performance improves with respect to trivial template matching.
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發(fā)表于 2025-3-27 15:40:26 | 只看該作者
Antichrist Obama and the Doomsday Preppers and testing, with fewer windows used in testing, and (3) merging with non-maximal suppression (NMS) in windows and pages has been replaced by merging overlapping detections using XY-cutting at the page level. Our fastest model processes 3 pages per second on a Linux system with a GTX 1080Ti GPU, Intel i7-7700K CPU, and 32 GB of RAM.
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發(fā)表于 2025-3-27 20:31:07 | 只看該作者
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發(fā)表于 2025-3-28 01:38:03 | 只看該作者
Conference proceedings 2021ition, ICDAR 2021, held in Lausanne, Switzerland, in September 2021.The total of 59 full and 12 short papers presented in this book were carefully selected from 96 contributions?and divided into two volumes. Part I contains 29 full and 4 short papers that stem from the following meetings: ICDAR 2021
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發(fā)表于 2025-3-28 05:50:15 | 只看該作者
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發(fā)表于 2025-3-28 06:40:54 | 只看該作者
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發(fā)表于 2025-3-28 12:50:12 | 只看該作者
Graph-Based Object Detection Enhancement for Symbolic Engineering Drawingsa graph representation of the extracted circuit components. The graph structure is then analysed using graph convolutional neural networks and node degree comparison to identify graph anomalies potentially resulting from false negatives from the object recognition module. Anomaly predictions are the
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