# Rethinking Learnable Proposals for Graphical Object Detection in Scanned Document Images
## 1 Introduction
Graphical page object detection is a challenging downstream domain adaptation task of generic object detection in the natural image domain. This fact is not lost on many researchers, and they have come up with many adapted models for graphical object detection [2,11]. Document images are sparse in contextual information as compared to natural images. The graphical page objects of different classes, such as tables, figures, and text, are logically clustered together in document images. The same is not always true with natural images. The different graphical page objects show a wider variety of scale than objects in natural images. Further, most top-performing deep learning models on natural images have strong and deep backbones with texture-driven region proposal networks (RPNs) [4]. By design, these RPNs propose regions in natural images rich in texture, contrast, and color. Selective search in Fast R-CNN [12], and the RPN in Faster R-CNN [4,13] are examples of such. The assumption of logical clustering may not always hold for natural images. Thus, it makes sense to have dense object candidates for the features maps to avoid missing objects in the image. When coupled with strong backbones, dense object candidates in document  
Figure 2: Challenges of graphical page object detection. Natural images are outlined in blue, logos in yellow, and signatures in orange. Natural images and signatures have text superimpositions in the second and third rows. The natural image in the fourth row has text and vector graphic superimposition. images can confuse the detector head. Hence, naive adaptation of models designed for dense natural images to the document image domain may not yield good results. Most state-of-the-art graphical page object detection models suffer from the aforementioned systemic challenges and issues. They fail to account for the differences in the domains in an intrinsic manner. Instead, they rely on extrinsic methods such as heavy pre/post-processing of images that are not genuinely end-to-end. In an attempt to address these systemic issues, this paper investigates the feasibility of sparse and learnable object candidates in scanned images of documents and attempts to justify the applicability of the same through extensive experimentation. This paper also investigates the relationship between meaningful gains and the feature extraction capabilities of deep backbones. The scope of the paper has been limited to investigating the effectiveness of deep backbones and learnable sparse proposals for the downstream task of graphical page object detection. Our experiments show that sparse proposals-based methods such as Sparse R-CNN come close to state-of-the-art methods while being computational cheaper. The main contribution of the paper can be summarized as follows:  
* Empirically shows that detectors based on dense object candidate priors are not well suited for graphical page object detection. Detectors that reduce the number of object candidate priors while also making them learnable are a better approach;
* Investigates the effectiveness of deep and robust backbones using the CBNetV2 [14] architecture with different detection heads and backbones, such as Dual Swin-Transformer tiny, small, base, and large;
* Experimentally shows that throwing deep and strong backbones or ensemble versions of these backbones does not lead to better detection results;
* Empirically show that Sparse R-CNN achieves comparable performance to state-of-the-art while being computationally efficient by a large margin;
* Investigates the generalization abilities of the ResNet-101 Sparse R-CNN [15] model by performing exhaustive cross-validation experiments. The remainder of this paper is structured as follows. Section 2 categorizes the prior work in the field into traditional approaches and deep learning approaches. Section 3 describes the deep backbones investigated, such as CBNetV2 (Section 3.1).