An Empirical Investigation of the Effectiveness of Optical Recognition of Hand-Drawn Business Process Elements by Applying Machine Learning

Business process diagrams represent an essential part of business process management activities, including process analysis, process-related communication, and process automation. While the majority of these artifacts are produced with modeling tools, there are still cases where hand-drawn diagrams are created, especially in the initial phases of process discovery and process innovation activities. The transformation of a hand-drawn diagram to digital format often presents several challenges. Thus, our efforts are directed towards investigating the effectiveness of an automatic transformation of hand-drawn diagrams into digital artifacts utilizing optical character recognition. To this aim, we performed empirical research in which subjects were instructed to redraw standardized process elements, which were afterward used for the training and testing set of machine-learning-based character recognition application. The findings obtained in the analysis showed that TensorFlow based and trained solution is capable of identifying hand-drawn process diagrams elements on different levels of accuracy. These insights may be considered when specifying or adapting the visual vocabularies of notations assuring appropriate visual distances between depictions of individual elements.