In the context of urban buildings, architectural floor plans describe a building's structure and spatial distribution. These digital documents are usually shared in file formats that discard the semantic information related to walls and rooms. This work proposes a new method to recover the structural information by extracting walls and detecting rooms in 2D floor plan images, aimed at multi-unit floor plans which present challenges of higher complexity than previous works. Our proposed approach is able to handle overlapped floor plan elements, notation variations and defects in the input image, and its speed makes it suitable for real applications on both desktop and mobile devices. We evaluate our methods in terms of precision and recall against our own annotated dataset of multi-unit floor plans. / Graduate

This paper tackles the challenge of how to compare floor plans with each other. A lot of different methods were used to analyze floor plan images, such as different kinds of pixel-based breadth-first search algorithms for finding walls, doors, and windows. Python-tesseract was used to read text labels in the floor plan, which was of great use when rooms were to be identified. The extracted information from over 1000 floor plans was then used to create a comparison program, which spits out the most similar floor plans to any given floor plan. The results of the extraction part were pretty good for most of the floor plans. Walls, doors, and windows were often accurately found, and the room identification worked very well compared to other known methods. Using the extracted data to find similar floor plans worked splendidly. The extraction part of the project had its flaws and can be improved, but even so, this method of assessing similarity between floor plans works very well. / Den här rapporten tacklar problemet och försöker ge ett svar på hur man kan jämföra planritningar med varandra. Flera olika metoder användes för att analysera planritningar, exempelvis olika typer av pixelbaserade sökalgoritmer för att hitta planritningens väggar, dörrar och fönster. Python-tesseract användes också för att läsa textetiketter i planritningen, vilket var till stor nytta när rummen skulle identifieras. Den extraherade informationen från över 1 000 planritningar användes sedan för att skapa ett jämförelseprogram, som spottar ut de 10 mest liknande planritningarna till en given planritning. Resultatet av extraheringsdelen var väldigt bra för de flesta planritningarna. Se exempelbilden nedan. Väggar, dörrar och fönster hittades ofta korrekt och rumidentifieringen fungerade mycket bra jämfört med andra kända metoder. Att använda den extraherade information för att sedan hitta liknande planlösningar fungerade utmärkt. Extraheringsdelen av projektet hade sina brister och kan förbättras, men trots det fungerar denna metod för att jämföra planlösningar väldigt bra.