Bridging the Performance Gap between DETR and R-CNN for Graphical Object
  Detection in Document Images
Tahira Shehzadi Khurram Azeem Hashmi Didier Stricker Marcus Liwicki Muhammad Zeshan Afzal
This paper takes an important step in bridging the performance gap between
DETR and R-CNN for graphical object detection. Existing graphical object
detection approaches have enjoyed recent enhancements in CNN-based object
detection methods, achieving remarkable progress. Recently, Transformer-based
detectors have considerably boosted the generic object detection performance,
eliminating the need for hand-crafted features or post-processing steps such as
Non-Maximum Suppression (NMS) using object queries. However, the effectiveness
of such enhanced transformer-based detection algorithms has yet to be verified
for the problem of graphical object detection. Essentially, inspired by the
latest advancements in the DETR, we employ the existing detection transformer
with few modifications for graphical object detection. We modify object queries
in different ways, using points, anchor boxes and adding positive and negative
noise to the anchors to boost performance. These modifications allow for better
handling of objects with varying sizes and aspect ratios, more robustness to
small variations in object positions and sizes, and improved image
discrimination between objects and non-objects. We evaluate our approach on the
four graphical datasets: PubTables, TableBank, NTable and PubLaynet. Upon
integrating query modifications in the DETR, we outperform prior works and
achieve new state-of-the-art results with the mAP of 96.9\%, 95.7\% and 99.3\%
on TableBank, PubLaynet, PubTables, respectively. The results from extensive
ablations show that transformer-based methods are more effective for document
analysis analogous to other applications. We hope this study draws more
attention to the research of using detection transformers in document image
analysis.