Relational models for visual understanding of graphical documents. Application to architectural drawings

In this thesis we face the graphical document understanding problem by proposing several relational models for the complete interpretation of floor plans. Firstly, we introduce three different strategies on symbol detection for walls, doors and windows. Secondly, we present two relational strategies that tackle the problem of the visual context extraction. Finally, we construct a knowledge-based model consisting of an ontological definition of the domain and real data. All the resources used in this thesis are freely available for research purposes.

[1]  Ernest Valveny,et al.  Wall Patch-Based Segmentation in Architectural Floorplans , 2011, 2011 International Conference on Document Analysis and Recognition.

[2]  Ernest Valveny,et al.  Unsupervised Wall Detector in Architectural Floor Plans , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[3]  Oriol Ramos Terrades,et al.  CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool , 2014, International Journal on Document Analysis and Recognition (IJDAR).

[4]  Ernest Valveny,et al.  Statistical segmentation and structural recognition for floor plan interpretation , 2013, International Journal on Document Analysis and Recognition (IJDAR).

[5]  Ernest Valveny,et al.  Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies , 2013, GREC.