# Rethinking Learnable Proposals for Graphical Object Detection in Scanned Document Images
## 4 Experimental Results
### Result and Discussion
#### 4.4.1 IIIT-AR-13K  
Section 3.1 discussed the compositing architecture for deep learning backbones, and Section 3.2 discussed the different types of detection heads this study aims to investigate. This section discusses the experimental results of the IIIT-AR-13K dataset. Establishing Baselines  
The first thing needed for any comparison is the establishment of strong baselines. To this end, under the pre-training fine-tuning paradigm baseline, Faster R-CNN, Mask R-CNN, and Cascade Mask R-CNN models are trained on the IIIT-AR-13K dataset. The training is per the implementation details noted in Section 4.3. Table 6 shows the mean average precision (mAP) at the Intersection over Union (IoU) threshold from 0.5 to 0.95 in accordance with the COCO evaluation protocol [46]. The table also shows the average precision (AP) at Intersection over Union (IoU) thresholds of 0.75 and 0.5. It is evident that Cascade Mask R-CNN establishes the strongest baseline for the IIIT-AR-13K dataset, reaching an mAP of 0.76. This strong performance is due to the cascading nature of the detection head. Cascade Mask R-CNN allows each subsequent head to be enhanced by the localizing distribution of the previous head, hence giving better performance on the higher IoU threshold values. Mask R-CNN and Faster R-CNN perform equivalently. The reasoning is that in document images, the masks for graphical objects are rectangular and hence do not add much additional information compared to bounding boxes. Figure 8 shows the detection results as true-positives and false-positives for the baseline models in Table 6. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Method**} & **bbox mAP** & **bbox AP** & **bbox AP** & **FLOPS** \\  & **(IoU = 0.50:0.95)** & **(IoU = 0.75)** & **(IoU = 0.50)** & **(GFLOPs)** \\ \hline Baseline Faster R-CNN & 0.747 & 0.864 & 0.930 & 206.68 \\ Baseline Mask R-CNN & 0.745 & 0.845 & 0.929 & 258.23 \\ Baseline Cascade Mask R-CNN & 0.769 & 0.879 & 0.928 & 389.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The established baseline results on the IIIT-AR-13k [9] dataset. Figure 8: Visualizing some detection results for the models in Table 6. First row: The first and second images show true positive and false positive detection results for the baseline Faster R-CNN, respectively. Second row: The top and bottom images show the true positive and false positive detection results for the baseline Cascade Mask R-CNN [36]. Analyzing Effects of Strong Backbones  
Using strong backbones for object detection in natural images generally increases detection accuracy. Deep backbones can extract fine features and patterns. These fine features and patterns then help construct a high-level semantic representation of the data by the network. These semantic representations play a crucial role in object detection in natural images. Nevertheless, the extraction of high-level semantic features is highly dependent on the depth of the network. Increasing the depth of the network and tackling the accuracy degradation problem is not as straightforward. To achieve this, many novel general-purpose deep backbones such as ResNet [47] and even backbones from natural language processing (NLP) such as Swin-Transformers have been proposed. More recently, ensemble methods that combine two or more general-purpose backbones such as CBNet [38] and CBNetv2 [14] have been proposed. The effects of strong backbones in the document image domain are investigated by applying Dual-backbone Swin-Transformer, and Dual-backbone ResNet50 backbones with different detection heads such as Faster R-CNN, Mask R-CNN, and Cascade Mask R-CNN. Table 7 shows the different combinations of backbones and detection heads explored. The core idea behind CBNetv2 was that using two identical backbones could lead to improved performance, as the two backbones will learn different features and aid in the detection of objects. Tables 6 and 7 show that the Dual-backbone version of Faster R-CNN performs marginally better than the baseline version.