# Improvement of Business Productivity by Applying Robotic Process Automation
## 3 Rationale of BDA (Business Documentation Automation)
### Review of Advanced Research for BDA  
Prior research on RPA based on the 4 th industry technology (e.g., AI, IoT, Robot, Blockchain, etc.) has been described only in its maturity and trend of each core element technology [20]. For BDA (Business Document Automation), the prior studies have been conducted in four directions; spelling checking, document generation, applying document standards, and document classification work based on text recognition. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Report** & **Planning Proposal** & **Project Proposal** \\ \hline number of pages & 1–10 pages & 1–20 pages & 100–3000 pages \\ human resource & 1–3 people & 1–3 people & 2–50 people \\  & fact-based status & fact analysis based & Suggesting business \\  & information & future plans & idea based on RFP \\ writing & & freestyle & the style complying \\ standard & without writing standards & writing standards & writing standards \\  & & & shown in RFP \\ quality check & logical validity & & document standard \\ \hline \hline \end{tabular}  
* These characteristics can be different based on the specific company and circumstance.  
\end{table} 
Table 2: Characteristics of business documents *. First of all, there are two main studies by [PERSON] (2020) and [PERSON] et al., (2020) [21,22]. [PERSON] has developed RGL (Restricted Grammar Language) for cloud-based agile document work. RGL consists of a writing rule set that performs real-time spelling corrections. The research of [PERSON] proposed a spell correction method using deep learning based on the evaluation of word relations between surrounding situations. The method can verify two types of spelling errors in terms of word spelling errors and contextual spelling errors. The second case is an automatic document generation, which means that it automatically creates a court document with standard form besides automatically generating texts with a free form like a novel. [PERSON] and [PERSON] (2020) used the case of a law firm to automatically generate documents first, even if they are of poor quality, and then manually correct them. They emphasize that it is better to improve manually the quality of paper rather than manually creating high-quality business documents [23]. It was possible since most legal and court documents have standardized rules. Regarding the application of web document standards as the third research case, [PERSON] et al., (2010) and [PERSON] et al., (2019) reported that the standard of business documents has changed from the existing EDI (Electronic Data Interchange) to XML (eXtensible Markup Language). In addition, a lot of unnecessary manual work occurs in creating XML-based invoices, contracts, and various graphs. Therefore, they proposed an RPA applying document standards to improve the productivity of document work are proposed [24,25]. [PERSON] et al., (2019) analyzed the standardization compliance of Web (World Wide Web) documents assumed as a business document and suggested a way to effectively correct the errors with RPA [26]. The fourth research case is the automation of document classification work through detection and recognition of text described in business documents using BDA. [PERSON] et al., (2017) describe a method for recognizing text described in graphic drawing documents [27], and [PERSON] et al., (2020) describe a method for text detection and recognition in the process of digitizing classical documents [28]. In addition, research was conducted to recognize the meaning of the recognized text and automatically classify business documents. [PERSON] (2002) proposes the method recognizing the abstract of a patent and then classifying the technology type by IPC (International Patent Classification). [PERSON] et al. (2020) describe a method of automatically classifying the types of documents through a neural network  
Figure 1: Process of project proposal correction by manpower.