# A Survey of Graphical Page Object Detection with Deep Neural Networks
## 3 Methodologies
### Faster R-CNN  
Recently, it has been the case that the improvement of object detection algorithms in the field of computer vision has a direct relation with the improvement of graphical page object detection in document images. Faster R-CNN [11] which is the improved version of Fast R-CNN [31] is a two-stage object detection network. Figure 6 illustrates the architecture of Faster R-CNN. In order to obtain a detailed explanation about the architecture, readers may refer to [11]. This section covers the approaches that detect the graphical page objects by exploiting the capabilities of Faster R-CNN [11].  
An image-based deep learning table detection approach was suggested by [PERSON] et al. [29] where they implemented Faster R-CNN for detection of tables in document images. The paper presents that the recently introduced object detectors dependent on Convolutional Neural Networks (CNN) can detect tables in document images. By leveraging back-bones like ZFNet [37] and VGG-16 [14], the authors have achieved promising results on ICDAR-13 dataset [38]. Their approach has also utilized the transfer learning technique  
Figure 5: Categorization of discussed methodologies in the paper. The problem of page object detection is tackled through employing various deep learning concepts. The explained approaches are divided conceptually.  
Figure 6: Explained architecture of Faster R-CNN. Image is obtained from [11].  
by using the pre-trained model on the Pascal-VOC dataset [39]. They also attempt table structure recognition along with table detection.  
[PERSON] et al. [30] published a method for page object detection, which involves detecting figures, formulas, and tables. Their technique makes use of an ensemble technique of Fast R-CNN [31], and Faster R-CNN [11]. They combined the region proposals obtained from Fast R-CNN and Faster R-CNN and then apply bounding box regression to boost performance. They have used the ICDAR-17 POD [5] dataset to benchmark their approach.  
The blend of traditional methods and deep learning networks is presented by [PERSON] et al. [40] to solve the problem of formula and figure detection in document images. The authors propose that instead of giving raw input images to object detection algorithms, transformed image representations yield better results. Connected component analysis (CC), distance transform, and color transform on the raw input images are performed and are subsequently processed using the Faster R-CNN model.  
[PERSON] et al. [33] have utilized a similar technique. They have used the image transformation method in which a Euclidean distance transform [41], linear distance transform [42], and max distance transform [43] are applied on blue, green, and red channels of the input image, respectively. This transformed image is further propagated to Faster R-CNN to identify and regress the tabular boundaries in document images.  
Another approach in which performance of two state-of-the-art object detection networks: Faster R-CNN [11] and Mask R-CNN [10] is compared on graphical page objects [32]. The article presents exhaustive evaluations on the detection of tables, formulas, and figures in document images. The paper's conclusion states that Mask R-CNN [10] is better suited to solve the problem of page object detection because of having extra components in the loss function.