# A Survey of Graphical Page Object Detection with Deep Neural Networks
###### Abstract  
In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that make the digitization of documents viable. Since the advent of deep learning, deep learning-based object detection performance has improved many folds. This work outlines and summarizes the deep learning approaches for detecting graphical page objects in document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements. deep neural network; document images; review paper; deep learning; performance evaluation; page object detection; graphical page objects; document image analysis; page segmentation 202115334 25 April 20212599 June 20212599 June 20212599 June 202125999 June 202125999 June 202125999 June 2021259999 June 202125  
With the recent surge of deep learning-based object detection algorithms in computer vision [10; 11; 12], a considerable amount of methods are developed that have formulated the problem of detecting graphical page objects in document images as an object detection problem. Furthermore, several datasets consisting of thousands of annotated scanned document images are also published. Although the approaches leveraging these datasets have significantly improved state-of-the-art, a consolidated comparison among these approaches is missing. In this survey paper, we have presented a thorough analysis of the recent state-of-the-art approaches that have approached the problem of graphical page object detection in  
Figure 1: Demonstration of the problem of graphical page object detection in document images. **First Row**: figure detection in document images. **Second Row**: detection of single and multiple formulas in document images. **Third Row**: localization of tabular areas in document images. The samples are taken from the dataset of ICDAR-17 POD [9]. scanned document images by employing deep neural networks. Since page objects can be of several types [13], we have covered the three most important page objects in document images [9]. These graphical page objects are referred to as table, formulas, and figures. This paper investigates how deep neural network-based approaches work on detecting these types of page objects. Therefore, we have covered the most relevant approaches that have produced state-of-the-art results in this domain. Some of the discussed approaches work only on a single page object, and some have covered all three of them. However, our primary focus is to provide a perspective about the outcome of deep learning-based approaches on graphical page object detection in document images. To summarize, our contributions are as follows:  
1. We present the comparisons between recently introduced algorithms for improving page object detection by highlighting their advantages and limitations. 2. We present a brief overview of the publicly available challenging datasets for graphical page object detection. 3. We provide an evaluative comparison among the state-of-the-art graphical page object detection systems. Figure 3 illustrates the complete flow of this survey paper, whereas the remainder of the paper is organized as follows: Section 2 presents a brief overview of the prior works that have exploited traditional approaches to detect graphical page objects. Section 3 explains all the approaches contributing to graphical page objects by leveraging deep learning methods.