# A Survey of Graphical Page Object Detection with Deep Neural Networks
## 2 Traditional Approaches  
The problem of graphical page object detection in documents is a well-recognized problem. Several approaches that employed traditional methods are introduced in this domain. Figure 4 illustrates the fundamental differences between the traditional approaches and the deep learning-based approaches. The traditional approaches leverage image processing techniques such as binarization and connected component analysis. Contrarily, deep learning-based methods utilize backbone CNN to generate the spatial feature maps from the document images.  
In order to implement table detection, the prior techniques [16; 17; 18] have defined a certain underlying structure for tables in a document. [PERSON] et al. [19] employed Optical Character Recognition (OCR) to extract tabular information. The method tried to recognize possible table areas by analyzing the keywords and white spaces. The main disadvantage  
Figure 4: Visual depiction of the basic differences between the traditional methods and the deep learning-based techniques. Traditional approaches rely heavily on image processing methods and custom heuristics whereas deep learning techniques leverage convolutional neural networks-based architectures. In deep learning approaches, spatial features from the document images are extracted from backbone networks such as VGG-16 [14] or ResNet [15]. These features are further propagated to region detection or segmentation networks to classify and localize page objects.  
Figure 3: The block diagram illustrating the complete flow of the survey paper.  
of this approach is that it is fully based on the presumptions regarding the tables' structure and the collection of the used keywords.  
[PERSON] et al. [20] proposed another approach in the field of table analysis. It utilizes distance between consecutive words to detect table lines. Subsequently, adjacent vertical lines are grouped with consecutive horizontal words to propose table entity candidates. However, the underlying assumption is that there can be a maximum of two columns in a table. Hence, three types of layouts (single, double, and mixed columns) are designed in this approach. The drawback of this method is that it is only applicable to a limited number of designed temples.  
[PERSON] et al. [21; 22; 23] introduced a system called T-Recs to extract tabular information from documents. Their method takes the word bounding boxes which are segregated to build a segmentation graph in a bottom-up manner. Their system is vulnerable to tables containing multi rows and columns.  
A method for detecting tables by calculating the intersection area between the vertical and horizontal lines was suggested by [PERSON] et al. [24]. The recreation of tables is then done by denoting corresponding vertical and horizontal lines related to intersection pairs. This approach presumes that a table should have ruling lines. A method for table detection by using Hidden Markov Models (HMMs) was suggested by [PERSON] et al. [25]. The method fetches text from PDF files by applying the pdftotext Linux utility. Then feature vectors are computed based upon the gaps present between the text. This approach can only be employed for non-raster PDF files that do not contain noisy data.  
A method for table detection under the assumption that tables in documents can contain only singular columns is proposed by [PERSON] et al. [26]. Another technique for table detection in heterogeneous documents was proposed by [PERSON] et al. [18]. This mechanism is built into an open-source Tesseract OCR engine [6]. Although these traditional approaches were effective on the documents with restricted layout variations, either they rely on the meta-data or highly depends on the post-processing methods involving custom heuristics. Furthermore, they fail to produce similar results on generic datasets. Therefore, it is essential to exploit recently proposed deep learning techniques to tackle the problem of graphical page object detection in document images.