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.

翻译：表格检测是文档分析中的关键任务，旨在精准识别并定位文档图像中的表格区域。尽管深度学习在该领域取得了显著进展，但通常需要大规模标注数据集才能实现有效训练。当前基于CNN的半监督表格检测方法在检测过程中依赖锚点生成机制和非极大值抑制（NMS），限制了训练效率。而基于Transformer的半监督技术采用一对一的匹配策略，会产生含噪声的伪标签，进而削弱整体效能。本研究提出了一种创新的基于Transformer的半监督表格检测器，通过融合一对一与一对多分配技术的新型匹配策略提升伪标签质量。该方法在训练初期显著增强了训练效率，为后续训练提供更优质的伪标签。我们在PubLayNet、ICADR-19和TableBank等基准数据集上对半监督方法进行全面评估，在仅使用30%标注数据的情况下，在TableBank（词）和PubLayNet上分别达到95.7%和97.9%的mAP，较此前最优半监督表格检测方法分别提升7.4和7.6个百分点。实验结果表明，本方法全面超越现有最优方法，展现出显著优势。这项研究标志着半监督表格检测方法的重要突破，为实际文档分析任务提供了更高效精准的解决方案。