# Rethinking Learnable Proposals for Graphical Object Detection in Scanned Document Images
## 4 Experimental Results
### Result and Discussion
#### 4.4.1 IIIT-AR-13K
This can be caused by document images being simpler in context than natural images, and the two parallel and identical backbones do not learn different features, hence do not aid each other significantly in detection. As seen in Table 7, the Dual-backbone architecture of CBNetv2 with different Swin-Transformers as backbones is explored. Apparently, with both Mask R-CNN and Cascade Mask R-CNN as detection heads, the results are considerably worse than the baselines. This can be caused by the fact that the extraction of fine features and patterns by the strong backbones ends up confusing the detection heads of the network. This means that, in the document domain, where the images are much less contextually rich than natural images, the naive use of deep and strong backbones degrades performance. Figure 9 shows the detection results for the models in Table 7.  
Further, due to the sparse nature of the document images, the application of detection heads that rely on dense object priors can lead to degraded performance. A better approach would be if the object priors were sparse and learnable. Sparse R-CNN achieves this. Table 8 shows the different ResNet backbones used along with the Sparse R-CNN detection head. The table also shows the Sparse R-CNN (SR-CNN) model. Even with a relatively shallower backbone of ResNet50, the Sparse R-CNN model outperforms the strongest baseline in Table 6.  
With a deeper backbone such as ResNet101, the best results on the IIIT-AR-13K dataset are achieved. This may be because Sparse R-CNN employs learnable proposal boxes using the learnable proposal features in the dynamic head of the Sparse R-CNN detection head. At the same time, the deep backbone extracts high-level features relevant for detection. Figure 9: Visualizing some detection results for the models in Table 7. First row: The two images show the detection results for Dual-Backbone Swin Transformer Tiny Mask R-CNN. Second row: The first image shows the detection results for Dual-Backbone Faster R-CNN, and the second shows the detection results for Dual-Backbone Swin-Transformer Small Cascade Mask R-CNN. As seen in Table 8, the authors in [39] evaluate the new one-stage YOLOF method on the IIIT-AR-13K. Their experimental YOLOF model achieves 0.588 mAP on the IIIT-AR-13K dataset. In comparison, the SR-CNN model achieves 35% higher raw mAP and considerably outperforms the model in the pre-training and fine-tuning paradigm. Further, Table 9 shows the effects of deep and robust backbones on the SR-CNN model. To this end, the Sparse R-CNN head is paired with the Swin-Transformer general-purpose backbone. The results saturate with the Swin-Transformer Base backbone, which is pre-trained on the ImageNet-1k dataset. Using more robust backbones trained on the ImageNet-22k dataset, such as Swin-Transformer Base and Swin-Transformer Large, results in degradation of the mean average precision (mAP). Figure 10 shows the graph of the F1 score vs. IoU thresholds for the SR-CNN model on the validation and test set split of the IIIT-AR-13K dataset. From the figure, it is evident that the the SR-CNN model performs well even on higher IoU thresholds for both the validation and test split of the dataset.