%0 Journal Article
%T Statistical Segmentation and Structural Recognition for Floor Plan Interpretation
%A Lluis Pere de las Heras
%A Ahmed Sheraz
%A Marcus Liwicki
%A Ernest Valveny
%A Gemma Sanchez
%J International Journal on Document Analysis and Recognition
%D 2014
%V 17
%N 3
%I Springer Berlin Heidelberg
%@ 1433-2833
%F Lluis Pere de las Heras2014
%O DAG; ADAS; 600.076; 600.077
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2370), last updated on Thu, 26 Feb 2015 17:01:21 +0100
%X A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents.
%U http://refbase.cvc.uab.es/files/HSL2013.pdf
%U http://dx.doi.org/10.1007/s10032-013-0215-2
%P 221-237