  Table detection within document images is a crucial task in document
processing, involving the identification and localization of tables. Recent
strides in deep learning have substantially improved the accuracy of this task,
but it still heavily relies on large labeled datasets for effective training.
Several semi-supervised approaches have emerged to overcome this challenge,
often employing CNN-based detectors with anchor proposals and post-processing
techniques like non-maximal suppression (NMS). However, recent advancements in
the field have shifted the focus towards transformer-based techniques,
eliminating the need for NMS and emphasizing object queries and attention
mechanisms. Previous research has focused on two key areas to improve
transformer-based detectors: refining the quality of object queries and
optimizing attention mechanisms. However, increasing object queries can
introduce redundancy, while adjustments to the attention mechanism can increase
complexity. To address these challenges, we introduce a semi-supervised
approach employing SAM-DETR, a novel approach for precise alignment between
object queries and target features. Our approach demonstrates remarkable
reductions in false positives and substantial enhancements in table detection
performance, particularly in complex documents characterized by diverse table
structures. This work provides more efficient and accurate table detection in
semi-supervised settings.
