  Table detection is the task of classifying and localizing table objects
within document images. With the recent development in deep learning methods,
we observe remarkable success in table detection. However, a significant amount
of labeled data is required to train these models effectively. Many
semi-supervised approaches are introduced to mitigate the need for a
substantial amount of label data. These approaches use CNN-based detectors that
rely on anchor proposals and post-processing stages such as NMS. To tackle
these limitations, this paper presents a novel end-to-end semi-supervised table
detection method that employs the deformable transformer for detecting table
objects. We evaluate our semi-supervised method on PubLayNet, DocBank, ICADR-19
and TableBank datasets, and it achieves superior performance compared to
previous methods. It outperforms the fully supervised method (Deformable
transformer) by +3.4 points on 10\% labels of TableBank-both dataset and the
previous CNN-based semi-supervised approach (Soft Teacher) by +1.8 points on
10\% labels of PubLayNet dataset. We hope this work opens new possibilities
towards semi-supervised and unsupervised table detection methods.
