# Rethinking Learnable Proposals for Graphical Object Detection in Scanned Document Images
## 5 Conclusions and Future Work  
This paper investigates the effectiveness of deep and robust backbones in the document image domain. Further, it also explores the idea of learnable object proposals through \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Method**} & **bbox mAP** & **bbox AP** & **bbox AP** & **GFLOPs** \\  & **(IoU = 0.50:0.95)** & **(IoU = 0.75)** & **(IoU = 0.50)** & **GFLOPs** \\ \hline F-RCNN [44] & 0.900 & - & - & 206.68 \\ M-RCNN [44] & 0.907 & - & - & 258.23 \\
**SR-CNN r101** & **0.937** & **0.962** & **0.981** & **225.97** \\ CDeC-Net [37] & 0.967 & - & - & 1375.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The results of the SR-CNN model on the PubLayNet [44] dataset, along with the comparison with other state-of-the-art methods. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{2}{c}{**Model**} & \multicolumn{3}{c}{**SR-CNN**} & \multicolumn{3}{c}{**Faster R-CNN**} \\ \hline \multirow{2}{*}{**Train Dataset**} & \multirow{2}{*}{**Test Dataset**} & \multicolumn{3}{c}{**Table**} & \multicolumn{3}{c}{**IoU [0.50:0.95]**} & \multirow{2}{*}{**mAP**} & \multicolumn{3}{c}{**Figure**} \\  & & **(AP)** & & & **(AP)** & & **(AP)** & **mAP** \\ \hline \multirow{2}{*}{IIIT-AR-13K} & \multicolumn{1}{c}{PublayNet} & 0.770 & 0.351 & 0.562 & 0.704 & 0.258 & 0.481 \\  & DocBank & 0.560 & 0.353 & 0.456 & 0.557 & 0.316 & 0.436 \\ \hline \multirow{2}{*}{PublayNet} & DocBank & 0.545 & 0.476 & 0.510 & 0.547 & 0.500 & 0.522 \\  & IIIT-AR-13K & 0.538 & 0.109 & 0.323 & 0.497 & 0.103 & 0.299 \\ \hline \multirow{2}{*}{DocBank} & IIIT-AR-13K & 0.293 & 0.075 & 0.184 & 0.399 & 0.083 & 0.241 \\  & PublayNet & 0.790 & 0.681 & 0.735 & 0.780 & 0.717 & 0.748 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The cross-dataset evaluation results for the SR-CNN and baseline Faster R-CNN models. We evaluate the models on the common classes of IIIT-AR-13k, PubLayNet, and DocBank. Sparse R-CNN. To this end, the Analyzing Effects of Strong Backbones subsection in Section 4.4.1 shows that naively throwing the best and strongest deep learning backbones at the object detection problem in documents will not improve results. The same is shown by experimenting with strong backbones such as Swin-Transformers and an ensemble method of combining two or more strong backbones via the CBNetV2 framework. These backbones are paired with various detection heads, such as Faster R-CNN [13], Mask R-CNN [30], and Cascade Mask R-CNN, and the degradation in results is evident in Table 7.  
Turning our attention to the detection head, where the argument is that it would be better if the object priors in the detection head were sparse and learnable. The Sparse R-CNN [15] detection head achieves this, formulating the SR-CNN model. The Learnable Proposals (Sparse R-CNN) subsection substantiates the above argument through experiments on the IIIT-AR-13k dataset and demonstrates that sparse and learnable proposals in the document image domain can lead to better performance. Table 9 also shows that the performance of Sparse R-CNN suffers when paired with a more robust and deeper backbone. This supports our claim that the naive use of strong backbones is not a great idea in document images. Inspired by this insight in Section 4.4.2, the SR-CNN model is trained on the Pub-LayNet dataset and achieves a mean average precision of 0.936. To the best of our knowledge, this is close to the current state-of-the-art of 0.96 (mAP) achieved by CDeC-Net [37]. The SR-CNN model is more computationally efficient (six times less GFLOPs as compared to CDeCNet) and uses the readily available COCO pre-trained weights. Table 1 shows the comparison of the key characteristics of the explored approaches along with their advantages and limitations to facilitate understanding. In future work, we plan to extend this idea to the more challenging domain of document structure recognition in document images.