  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:
https://github.com/open-mmlab/mmrotate/tree/dev-1.x/projects/RR360.
