# HybridTabNet: Towards Better Table Detection in Scanned Document Images
## 2 Related Work
### Learning-Based Approaches  
[PERSON] and [PERSON] [40] introduced T-Recs, which is a clustering approach to detect tables in documents. Later, in a follow up work, PDF-TREX [41] is proposed. This method applied T-Recs to extract tables from PDF documents. Along with unsupervised learning [40; 42], supervised learning was exploited to detect tables in documents [43]. The proposed system, Tabfinder, transforms a document into an MXY tree representation. Subsequently, the method proposes the possible tables by looking for the blocks that are enclosed in vertical and horizontal ruling lines. Hidden Markov Models (HMMs) [44; 45] and the combination of SVM classifier and custom heuristics [46] has also been exploited to produce table detection methods that depend on visible ruling lines in tables. Although machine learning-based methods have improved the performance of table detection systems, they either rely on the additional meta-data information or tables having specific layouts, such as the presence of ruling lines and so on. With the recent surge of deep learning-based algorithms in computer vision, a similar trend can be seen in the table understanding community. To begin with, [PERSON] et al. [47] implemented a deep Convolutional Neural Network (CNN) to extract spatial features, which were later combined with custom heuristics and meta-information from PDF to classify the tabular regions in documents. Later object detection algorithms [10; 14; 15; 16; 17] were heavily explored to develop robust and data-driven image-based table detection systems [6; 8; 9; 10; 11; 12; 28; 30]. [PERSON] et al. [11] employed Faster R-CNN [15] to detect tables in document images. In this work, the raw document images were first transformed by modifying their pixel values using the distance transform mechanism. These transformed images were fed to the object detection network to aid the process of recognizing tabular structures. An end-to-end image based table detection method was published by [PERSON] et al. [6]. The proposed method exploited Faster R-CNN [15] with a pretrained backbone (ZFNet [48] and VGG-16 [49]). The system GOD (Graphical Object Detection) [12] is an object detection framework that detects graphical page objects in document images. In the proposed work, the author empirically claimed that Mask R-CNN [16] worked better as compared to Faster R-CNN [15] in recognizing graphical page objects in scanned document images. A similar conclusion was presented by [PERSON] et al. [50] in which their novel proposed dataset PubLayNet was evaluated on both Faster and Mask R-CNN. Instead of conventional convolutions, [PERSON] et al. [7] employed deformable convolutions to detect tables in document images. The authors empirically established that the dynamic receptive field of deformable convolutions adapted better in detecting tabular boundaries with arbitrary layouts. In another work, Faster R-CNN [29] was employed to tackle the problem of table detection in document images. The final tabular area was retrieved by refining the coroners of predicted tabular boundaries. [PERSON] et al. [30] presented an ensembling technique in which Fast R-CNN [14] and Faster R-CNN [15] were combined to detect graphical page objects in document images. Since tabular images are limited in number, several of the above-mentioned approaches leverage fine-tuning techniques [6; 7; 11]. In one of the recent works [28], it has been proposed that close-domain fine-tuning performs better as compared to open-domain fine-tuning for detecting tables in document images. In order to establish this conclusion, the authors exploited Mask R-CNN [16], RetinaNet [51], SSD [52], and YOLO [53] to perform the task of table detection. CascadeTabNet [8] is an end-to-end table detection system that operates on Cascade Mask R-CNN, which is an extension of Cascade R-CNN [17].