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Titlebook: Document Analysis and Recognition - ICDAR 2024; 18th International C Elisa H. Barney Smith,Marcus Liwicki,Liangrui Peng Conference proceedi

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41#
發(fā)表于 2025-3-28 18:12:41 | 只看該作者
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發(fā)表于 2025-3-28 19:45:23 | 只看該作者
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發(fā)表于 2025-3-29 02:00:34 | 只看該作者
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發(fā)表于 2025-3-29 05:24:58 | 只看該作者
45#
發(fā)表于 2025-3-29 11:00:24 | 只看該作者
Improving Efficiency and?Performance Through CTC-Based Transformers for?Mathematical Expression Reco, this approach outperforms most state-of-the-art systems on mark-up-level and image-level metrics across both datasets. A comprehensive study demonstrates the model’s capability to interpret complex reading orders of mathematical expressions, showing that the monotonicity of the CTC alignments is n
46#
發(fā)表于 2025-3-29 12:03:43 | 只看該作者
ICAL: Implicit Character-Aided Learning for?Enhanced Handwritten Mathematical Expression Recognitionion recognition rate (ExpRate) by 2.25%/1.81%/1.39% on the CROHME 2014/2016/2019 datasets respectively, and achieves a remarkable 69.06% on the challenging HME100k test set. We make our code available on the GitHub. (.).
47#
發(fā)表于 2025-3-29 17:19:41 | 只看該作者
Stroke-Level Graph Labeling with?Edge-Weighted Graph Attention Network for?Handwritten Mathematical ure extraction, and produces strokes and relations classification. Experiments show that our proposed EGAT algorithm can effectively fuse the node features as well as the weighted edge features, and predict the node and edge attributes simultaneously.
48#
發(fā)表于 2025-3-29 22:25:05 | 只看該作者
A Real-Time Scene Uyghur Text Detection Network Based on?Feature Complementationk based on spatial feature attention (SFA-FEN) is included in to obtain multi-target information by expanding the range of perceptual fields and enhancing the robustness of small-scale Uyghur. The experimental results show that FC-Net can maintain the advantages of a lightweight network while mainta
49#
發(fā)表于 2025-3-29 23:58:10 | 只看該作者
LMTextSpotter: Towards Better Scene Text Spotting with?Language Modeling in?Transformerrelying on the character level annotations. After training phase, the LM recognition branch can predict the character classes independently of the CTC recognition branch. Besides, we exploit the position priors provided by rotated bounding boxes and adopt a task-specific query strategy for further i
50#
發(fā)表于 2025-3-30 04:27:28 | 只看該作者
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