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Josep Llados, Horst Bunke and Enric Marti. 1996. Structural Recognition of hand drawn floor plans. VI National Symposium on Pattern Recognition and Image Analysis. Cordoba.
Abstract: A system to recognize hand drawn architectural drawings in a CAD environment has been deve- loped. In this paper we focus on its high level interpretation module. To interpret a floor plan, the system must identify several building elements, whose description is stored in a library of pat- terns, as well as their spatial relationships. We propose a structural approach based on subgraph isomorphism techniques to obtain a high-level interpretation of the document. The vectorized input document and the patterns to be recognized are represented by attributed graphs. Discrete relaxation techniques (AC4 algorithm) have been applied to develop the matching algorithm. The process has been divided in three steps: node labeling, local consistency and global consistency verification. The hand drawn creation causes disturbed line drawings with several accuracy errors, which must be taken into account. Here we have identified them and the AC4 algorithm has been adapted to manage them.
Keywords: Rotational Symmetry; Reflectional Symmetry; String Matching.
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Josep Llados, Gemma Sanchez and K. Tombre. 2002. An Error-Correction Graph Grammar to Recognize Texture Symbols..
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Josep Llados, Gemma Sanchez and Enric Marti. 1997. A String-Based Method to Recognize Symbols and Structural Textures in Architectural Plans. Graphics Recognition Algorithms and Systems. GREC 1997..91–103. (LNCS.)
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Josep Llados, Gemma Sanchez and Enric Marti. 1998. A string based method to recognize symbols and structural textures in architectural plans. Graphics Recognition Algorithms and Systems Second International Workshop, GREC' 97 Nancy, France, August 22–23, 1997 Selected Papers. Springer Link, 91–103. (LNCS.)
Abstract: This paper deals with the recognition of symbols and structural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clustering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the similarity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion.
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Josep Llados and Gemma Sanchez. 2003. Symbol Recognition Using Graphs.
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Josep Llados and Gemma Sanchez. 2004. Graph Matching vs. Graph Parsing in Graphics Recognition: A Combined Approach.
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Josep Llados and Gemma Sanchez. 2007. Indexing Historical Documents by Word Shape Signatures. 9th International Conference on Document Analysis and Recognition.362–366.
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Josep Llados, Felipe Lumbreras, V. Chapaprieta and J. Queralt. 2001. ICAR: Identity Card Automatic Reader..
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Josep Llados, Felipe Lumbreras and Javier Varona. 1999. A multidocument platform for automatic reading of identity cards..
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Josep Llados, Ernest Valveny, Gemma Sanchez and Enric Marti. 2002. Symbol recognition: current advances and perspectives. In Dorothea Blostein and Young- Bin Kwon, ed. Graphics Recognition Algorithms And Applications. Springer-Verlag, 104–128. (LNCS.)
Abstract: The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.
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