AbstractFloor plan vectorization is an emerging research area in geographic information science and computer vision. However, automated recognition of building elements remains a challenge. This work proposes a method that combines the advantages of classical graphics with deep learning. Specifically, a morphological template is introduced to optimize topological relations, enhance completeness, and suppress conflicts. Bezier curves are utilized to represent irregularity contributing to improving visual effects and experimental accuracy. Thus, the proposed method can be directly practiced to boost performance and correct pseudo-samples in self-training. Experiments demonstrate that the proposed method achieves a considerable improvement in CVC-FP and R2V benchmarks. Additionally, our approach outputs instances with consistent topology, enabling direct modeling into Industry Foundation Classes (IFC) or City Geography Markup Language (CityGML). Hopefully, this work can serve as a new baseline for further study.Practical ApplicationsAutomatic floor plan recognition has many potential applications. From one perspective, structural elements (walls, doors, and windows) are the most typical objects as they illustrate the primary layout of a building and convey essential information to deploy other components. Identifying structural elements is particularly crucial because it provides design, investigation, and assessment representations. From another perspective, retrieving the room types plays a critical role given that it offers the semantics of a scene. Identifying these elements can avoid tedious secondary measurements and even recover the structure from an advertising paper or poster, facilitating the subsequent application deployment. The applications in Building Information Modeling reconstruction (such as 3D models from 2D maps, architectural arrangement, structural redesign, virtual reality, indoor navigation and modeling, 3D reconstruction from interior photographs, bearing structure analysis, renovation, refurbishment, plan illustration interpretation, apartment price estimation, and accessibility for visually impaired people) could provide substantial support for the smart city.