Traditional models focus on horizontal table detection but struggle in
rotating contexts, limiting progress in table recognition. This paper
introduces a new task: detecting table regions and localizing head-tail parts
in rotation scenarios. We propose corresponding datasets, evaluation metrics,
and methods. Our novel method, 'Adaptively Bounded Rotation,' addresses dataset
scarcity in detecting rotated tables and their head-tail parts. We produced
'TRR360D,' a dataset incorporating semantic information of table head and tail,
based on 'ICDAR2019MTD.' A new metric, 'R360 AP,' measures precision in
detecting rotated regions and localizing head-tail parts. Our baseline, the
high-speed and accurate 'RTMDet-S,' is chosen after extensive review and
testing. We introduce 'RTHDet,' enhancing the baseline with a 'r360' rotated
rectangle angle representation and an 'Angle Loss' branch, improving head-tail
localization. By applying transfer learning and adaptive boundary rotation
augmentation, RTHDet's AP50 (T<90) improved from 23.7% to 88.7% compared to the
baseline. This demonstrates RTHDet's effectiveness in detecting rotating table
regions and accurately localizing head and tail parts.RTHDet is integrated into
the widely-used open-source MMRotate toolkit:
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本文介绍了一项新任务：在旋转场景中检测表格区域并定位首尾部分，提出了相应的数据集、评估指标和方法。通过应用迁移学习和自适应边界旋转增强，提出的方法能够有效地检测旋转的表格区域并准确地定位头部和尾部部分。