# A Survey of Graphical Page Object Detection with Deep Neural Networks
## 3 Methodologies  
Graphical objects like tables, figures, and formulas are an integral part of documents because they hold a significant amount of information in a confined space. As explained in Section 1, detecting the graphical object means localizing these objects within a document image. Conceptually this problem is identical to localizing the objects in natural scene images. Recently, deep learning algorithms have also attracted the interest of researchers in the document image analysis community.  
This section will discuss the methodologies that have utilized the capabilities of deep neural networks to solve the problem of graphical page object detection in document images. By following the convention of [5], we have covered approaches that have worked on the detection of the following graphical page objects in document images: (1) Tables, (2) Figures, and (3) Formulas.  
For the convenience of our readers, we have classified the methodologies according to the employed deep learning concepts. We discuss the organizational flow of the methodologies in Figure 5. Table 1 summarizes the presented approaches and highlights their advantages and limitations.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Literature** & **Method** & **Advantages** & **Disadvantages** \\ \hline De-CNT [27] & Deformable convolutions, implemented in the Faster R-CNN [11] architecture. & The dynamic receptive field of deformable CNN helps recognize tabular boundaries having arbitrary layouts. & Deformable CNN requires more computation as compared to conventional CNN. \\ \hline Fi-Fo Detector [28] & Color transform, connected component analysis, distance transform applied on images that are fed to deformable pyramid network. & **(a)** Transformed images yield better results as compared to raw input images. **(b)** The approach leverages the deformable FPN model in their object detection network. & The approach depends on the extra pre-processing steps. \\ \hline DeepDeSRT [29] & Faster R-CNN [11] with transfer learning. & Straightforward and effective approach to detect tables. & Does not perform as accurate as other state-of-the-art methods. \\ \hline [PERSON] et al. [30] & Ensembling of Fast R-CNN [31] and Faster R-CNN [11]. & Leveraging the power of both selective search and region proposal network to generate reliable region of interests. & Computationally expensive because of combination of two separate object detection networks. \\ \hline GOD [32] & Faster R-CNN [11] and Mask R-CNN [10]. & Simple end-to-end approach to detect multiple page objects. & The network often mis-classifies the similar-looking page objects belonging to different classes. \\ \hline [PERSON] et al. [33] & Transformed images are passed through Faster R-CNN [11]. & The distance transform method helps the object detection network to focus on desired page object. & Requires extra pre-processing method. \\ \hline CDeC-Net [34] & Cascaded Mask R-CNN [12]. & **(a)** Multi-scale feature pyramid network. **(b)** Deformable Convolution improves the performance. & The method requires high computational resources due to composite backbone and deformable convolutions. \\ \hline [PERSON] et al. [35] & Semantic image segmentation with saliency detection. & **(a)** Table detection formulated as a task of saliency detection. **(b)** Dilated convolutions instead of traditional convolution improve efficiency. & The method requires high computational resources due to composite backbone and deformable convolutions. \\ \hline [PERSON] et al. [36] & Dynamic programming based technique. & Replacing non maximal suppression with dynamic programming algorithm improves the refining process for region of interests. & Extra post-processing over head. \\ \hline \hline \end{tabular}
\end{table}
Table 1: A Summary of different graphical page object detection methods that have employed deep neural networks.