# Rethinking Learnable Proposals for Graphical Object Detection in Scanned Document Images
## 2 Related Work
### Deep Learning-Based Approaches  
In deep learning, graphical object detection is seen as a downstream task of generic object detection in natural images. One of the first works to adapt deep learning models from the natural image domain to the downstream task of graphical object detection was by [PERSON] et al. [23]. They applied Faster R-CNN to detect tables in document images. The document images are first transformed per pixel using a distance transformation mechanism. These transformed images are then processed by the Faster R-CNN model to aid in table structure recognition. [PERSON] et al. [24] also apply Faster R-CNN [13] for detecting tables in scanned document images. They also proposed a novel deep learning approach for table structure recognition. In their work, they employ pre-trained backbones and showed that object detectors from the natural image domain could be successfully adapted for the document image domain on the ICDAR-13 dataset [25]. Following this in 2018, [PERSON] et al. [26] proposed to combine Fast R-CNN [12] and Faster R-CNN using an ensemble paradigm to capitalize on the benefits of both object detectors. They achieved this by taking the region proposal from both Fast R-CNN and Faster R-CNN and applying the bounding box regression on their combination. They applied their ensemble model to the graphical page object detection problem and achieved promising results. By now, domain adaptation from the natural image domain to the document image domain had become quite popular. [PERSON] et al. [27] proposed DeCNT, which employed deformable convolutions to detect tables in document images. The authors empirically showed that the dynamic receptive field of the network due to the deformable convolutions can adapt better to the dynamic layout of tables in documents. [PERSON] et al. [28] also applied Faster R-CNN for table detection, where the tabular area is retrieved by refining the coarse table detection and corner location produced by Faster R-CNN. This refining is achieved by grouping the corner belonging to the same table by coordinate matching and filtering out unreliable corners. In 2019, [PERSON] et al. [29] proposed the GOD framework in which they investigated the performance of Faster R-CNN and Mask R-CNN [30]. They compared the two detectors for graphical page object detection in the document images. After exhaustively evaluating the two detectors, they concluded that Mask R-CNN performs better for graphical page object detection than Faster R-CNN. Detection is the basis for more complex tasks such as table structure recognition, in which tables are first detected and then the structure is also extracted. To this end, [PERSON] et al. [31] proposed TabbyPDF, which is a web-based system for detecting and extracting structures of tables located in PDF documents. The system uses a heuristic-basedapproach for table detection and structure recognition. Bordered tables are detected using rule lines that compose a rectangular frame, whereas border-less tables are detected using a bottom-up segmentation approach. The authors evaluate their approach on the ICDAR-13 dataset. [PERSON] et al. [32] proposed GraphTSR, a graph neural network for recognizing the table structure in PDF files. First, through pre-processing, the cell contents and their corresponding bounding boxes are extracted from the PDF, then an undirected graph is produced using the cells, the edges of which are filtered using adjacent relation prediction through the attention mechanism. Finally, the predicted table structure is extracted from the labeled graph. TabStruct-Net, proposed by [PERSON] et al. [33], approaches table structure detection as a relationship problem between the row and column of the detected cell. First, a ResNet backbone is used to detect cells from the table. Next, an alignment loss function is introduced to ensure that detected cells belong to the same row or column, followed by the post-processing step of producing an XML output of the table structure.