RTHDet: Rotate Table Area and Head Detection in images
<|reference_start|>RTHDet: Rotate Table Area and Head Detection in images: 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.<|reference_end|>
@article{hu2023rthdet:,
    title={RTHDet: Rotate Table Area and Head Detection in images},
    author={Wenxing Hu, Minglei Tong},
    journal={arXiv preprint arXiv:2402.03315},
    year={2023},
    archivePrefix={arXiv},
    eprint={2402.03315},
    primaryClass={cs.CV}
}