# Rethinking Learnable Proposals for Graphical Object Detection in Scanned Document Images
## 1 Introduction  
We live amid the digital age, surrounded by the digital universe, which is expanding with the daily data we produce. In this digital age, we are creating, consuming, and replicating data like never before [1]. The estimates tell us that from 2005 to 2020, our digital universe has grown 300 times, from 130 exabytes to 40,000 exabytes of data. It will continue to double every year. It is also estimated that as much as 33% of the digital universe contains information that could be potentially valuable if analyzed. Further, almost 40% of the data produced will be touched by the cloud in some way or another. Manually processing such large amounts of data is laborious, time-consuming, and borderline infeasible. This presents an excellent opportunity for data mining and analysis. To this end, the field of document digitization has received considerable attention and made rapid strides in recent years [2]. Document digitization is the automatic extraction of meaningful information from scanned document images. Information is stored in two major formats in scanned document images: text and graphical page objects. Graphical page objects can be tables, figures, natural images, equations, formulas, logos, etc. [2; 3; 4]. They contain valuable information, but this information can only be extracted when these objects are correctly identified in the respective images [5]. Figure 1 shows the importance of detecting graphical page objects before extracting information. Graphical page objects have high intra-class variations in shape, type, and scale [2; 6]. Tables come in different shapes, sizes, and layouts (bordered or borderless) [3; 6; 7; 8]. There are also significant amounts of inter-class similarities [6]. Further, natural images can have superimpositions of vectors or diagrams. Figures can be arbitrary or abstractions of natural images. Equations and formulas can be single or multiple lines. A significant inter-mixing of classes can also occur, for instance, a logo inside a table or figure. Figure 2 illustrates the challenges in graphical page object detection. Revenues by geography are based on the \"filling\" address of the advertiser. The following table sets forth revenues and long-lived assets by geographic area (in thousands): \begin{tabular}{c c c c}  & \multicolumn{3}{c}{**Year Roded Dosember 31,**} \\ \cline{2-4}  & 2002 & 2003 & 2004 \\ \cline{2-4} Revenues & \$341,570 & \$1,038,409 & \$2,119,043 \\ \cline{2-4} International & \$97,938 & \$427,525 & \$1,070,180 \\ \cline{2-4} Total revenues & \$439,508 & \$1,465,934 & \$3,189,223 \\ \cline{2-4}  & \multicolumn{3}{c}{**As of December 31,**} \\ \cline{2-4}  & 2001 & 2003 & 2004 \\ \cline{2-4} Long-lived assets: & \$55,009 & \$267,346 & \$552,857 \\ \cline{2-4} International & \$87 & \$43,876 & \$67,029 \\ \cline{2-4} Total long-lived assets & \$55,096 & \$311,224 & \$619,886 \\ \cline{2-4} Google Inc. & \multicolumn{3}{c}{**Notes TO CONSOLidated FINANCI STATEMENTS--(Continued)**} \\ \end{tabular}   
Revenues by geography are based on the \"filling\" address of the advertiser. The following table sets forth revenues and long-lived assets by geographic area (in thousands): \begin{tabular}{c c c}  & \$341,570 & \$1,038,409 & \$2,119,043 \\ \cline{2-4}  & \$97,938 & \$2,7525 & \$1,070,180 \\ \cline{2-4}  & \$267,348 & \$552,857 \\ \cline{2-4}  & \$43876 & \$7,029 \\ \cline{2-4}  & \$555,096 & \$311224 & \$619,886 \\ \end{tabular}   
* **Note 14. Subsequent Events**
* **Rescission Offer** (b)  
Figure 1: This figure shows the importance of detecting graphical page objects: (**a**) shows a document image from the IIIT-AR-13k [9] dataset. In contrast, (**b**) shows the result of Tesseract-OCR [10] on the document image. Note that the naive application of Tesseract-OCR on the document image leads to poor extraction of information. Deep learning has been one of the most popular and explored solutions to object detection in the natural image domain. In recent years, it has seen rapid improvements.