Recognition of Paper-Based Conceptual Models Captured Under Uncontrolled Conditions

Abstract

Today, modeling and subsequent digital model representations are essential constituents in collaborative endeavors on organizational change. Once created, models need to be digitized for shared stakeholder understanding and further processing. Whenever paper serves as carrier medium, it is likely to disrupt further processing, elicitation, and modeling. While digital environments support transformation processes in collaborative modeling from its very beginning, the necessary technical infrastructure still might hamper situated capturing of models. Hence, this contribution aims to reduce the need for sophisticated technical components by enabling stakeholders to capture their paper-based models in a situation-sensitive way. We present a system that enables capturing paper-based models with mundane technical means by end users under uncontrolled conditions. We describe the components developed for recognition of these models and embed it in a mixed-modality workflow supported by a tabletop interfacing a web platform for further processing. As our empirical evidence demonstrates, this approach enables both situated and error-tolerant capturing of hand-drawn conceptual models by individual users. Moreover, it can be integrated with more sophisticated IT-based modeling tools for further digital processing.