## 1 Introduction  
In the data-driven age of today it is imperative for businesses to utilize their data in an efficient and timely manner in order to gain a competitive advantage. One of the major problems many organizations face however, is that most of their data assets (\(\pm\)80%) are in a semi- or unstructured format [1; 2; 3] resulting in an investment in costly natural language processing techniques in order to digitize the data [4]. The aim of this research is to provide a practical business automation workflow that can be implemented or integrated into an existing system in order to produce accurate digitized image data, while avoiding the possible errors introduced by manual entry of crucial identity attributes of entities [5]. Digitization is the process of turning information into a form that can be read easily by a computer [4]. Many research papers cover the use of OCR (Optical Character Recognition) technologies available on the market today as well as provide comparisons between the various technologies [6; 7; 8]. The research conducted for this paper specifically focused on automating, validating, and accurately preparingdesired data for use in an existing business process or technology. More detailed discussions about the motivation for the workflow as well as each step built into the workflow will be discussed in the 'Related Work' section below. The novel full image digitization workflow process as suggested by the authors can be seen below (Figure 1). Each step will be discussed and justified in detail and was designed to overcome the limitations or problem areas of the step before it. The raw extracted text that is generated from the OCR process forms the starting point of the business process automation. Predefined domain-specific field labels are used in the process of identifying field names from the extracted text. This is similar to the approach of [PERSON] et al. [9] that also only used a dictionary of domain-specific words during pattern matching to narrow down the possible candidates of words to match against. The predefined field labels are maintained in an external document alias file with the corresponding regular expression as a matching field-value. Regular expressions were chosen as a means to help identify candidate values for a field as the values for each key share a common structure but cannot be expressed in a finite dictionary. Key-value matching becomes more complicated where there are multiple string values in the OCR output file that conform to a specific regular expression pattern [10]. The solution to this was to use the field name position on the document and to match the first successful pattern that was located closest to the field name. There is very little literature that takes this approach, as the focus of many of the studies was simply to suggest corrections on a GUI (graphical user interface) [9] or to measure correctness of the OCR tools. Other studies aimed to determine which OCR tools performed the best and give a quantitative result [6; 7; 8]. The goal of this study was to produce a working system that generated the key-value pair dictionary for system consumption. To further improve the accuracy of the matches, ASM (approximate string matching) was added to the process in order to ensure that provision was made for small reading errors [11]. Once the program had made the key-value matches, it created a dictionary in JSON format as a final result. With the output structure being a JSON dictionary, any system that can consume such a dictionary would be able to use this process as an initial step in digitizing data. A key objective was to maximize the amount of correct key-value matches (where the key and its corresponding value were both correctly read from the images and paired together correctly), and to minimize incorrect key-value matches (where a key would be matched with the incorrect value or where a key would be matched with no value).