  Table detection, a pivotal task in document analysis, aims to precisely
recognize and locate tables within document images. Although deep learning has
shown remarkable progress in this realm, it typically requires an extensive
dataset of labeled data for proficient training. Current CNN-based
semi-supervised table detection approaches use the anchor generation process
and Non-Maximum Suppression (NMS) in their detection process, limiting training
efficiency. Meanwhile, transformer-based semi-supervised techniques adopted a
one-to-one match strategy that provides noisy pseudo-labels, limiting overall
efficiency. This study presents an innovative transformer-based semi-supervised
table detector. It improves the quality of pseudo-labels through a novel
matching strategy combining one-to-one and one-to-many assignment techniques.
This approach significantly enhances training efficiency during the early
stages, ensuring superior pseudo-labels for further training. Our
semi-supervised approach is comprehensively evaluated on benchmark datasets,
including PubLayNet, ICADR-19, and TableBank. It achieves new state-of-the-art
results, with a mAP of 95.7% and 97.9% on TableBank (word) and PubLaynet with
30% label data, marking a 7.4 and 7.6 point improvement over previous
semi-supervised table detection approach, respectively. The results clearly
show the superiority of our semi-supervised approach, surpassing all existing
state-of-the-art methods by substantial margins. This research represents a
significant advancement in semi-supervised table detection methods, offering a
more efficient and accurate solution for practical document analysis tasks.
