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Ernest Valveny and Enric Marti. 2000. Hand-drawn symbol recognition in graphic documents using deformable template matching and a Bayesian framework. Proc. 15th Int Pattern Recognition Conf.239–242.
Abstract: Hand-drawn symbols can take many different and distorted shapes from their ideal representation. Then, very flexible methods are needed to be able to handle unconstrained drawings. We propose here to extend our previous work in hand-drawn symbol recognition based on a Bayesian framework and deformable template matching. This approach gets flexibility enough to fit distorted shapes in the drawing while keeping fidelity to the ideal shape of the symbol. In this work, we define the similarity measure between an image and a symbol based on the distance from every pixel in the image to the lines in the symbol. Matching is carried out using an implementation of the EM algorithm. Thus, we can improve recognition rates and computation time with respect to our previous formulation based on a simulated annealing algorithm.
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Ernest Valveny and Enric Marti. 1999. Application of deformable template matching to symbol recognition in hand-written architectural draw. Proceedings of the Fifth International Conference on. Bangalore (India).
Abstract: We propose to use deformable template matching as a new approach to recognize characters and lineal symbols in hand-written line drawings, instead of traditional methods based on vectorization and feature extraction. Bayesian formulation of the deformable template matching allows combining fidelity to the ideal shape of the symbol with maximum flexibility to get the best fit to the input image. Lineal nature of symbols can be exploited to define a suitable representation of models and the set of deformations to be applied to them. Matching, however, is done over the original binary image to avoid losing relevant features during vectorization. We have applied this method to hand-written architectural drawings and experimental results demonstrate that symbols with high distortions from ideal shape can be accurately identified.
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Ernest Valveny and Enric Marti. 1999. Recognition of lineal symbols in hand-written drawings using deformable template matching. Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Análisis de Imágenes.
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Ernest Valveny and Enric Marti. 1997. Dimensions analysis in hand-drawn architectural drawings. VII National Simposium of Pattern Recognition and image Analysis, SNRFAI´97. CVC-UAB, 90–91.
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Gemma Sanchez and 6 others. 2003. A system for virtual prototyping of architectural projects. Proceedings of Fifth IAPR International Workshop on Pattern Recognition.65–74.
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Gemma Sanchez, Josep Llados and Enric Marti. 1997. Segmentation and analysis of linial texture in plans. Intelligence Artificielle et Complexité.. Paris.
Abstract: The problem of texture segmentation and interpretation is one of the main concerns in the field of document analysis. Graphical documents often contain areas characterized by a structural texture whose recognition allows both the document understanding, and its storage in a more compact way. In this work, we focus on structural linial textures of regular repetition contained in plan documents. Starting from an atributed graph which represents the vectorized input image, we develop a method to segment textured areas and recognize their placement rules. We wish to emphasize that the searched textures do not follow a predefined pattern. Minimal closed loops of the input graph are computed, and then hierarchically clustered. In this hierarchical clustering, a distance function between two closed loops is defined in terms of their areas difference and boundary resemblance computed by a string matching procedure. Finally it is noted that, when the texture consists of isolated primitive elements, the same method can be used after computing a Voronoi Tesselation of the input graph.
Keywords: Structural Texture, Voronoi, Hierarchical Clustering, String Matching.
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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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Gemma Sanchez, Josep Llados and Enric Marti. 1997. A string-based method to recognize symbols and structural textures in architectural plans. 2nd IAPR Workshop on Graphics Recognition.
Abstract: This paper deals with the recognition of symbols and struc- tural 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 clus- tering 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 simila- rity 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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Ernest Valveny and Enric Marti. 2000. Deformable Template Matching within a Bayesian Framework for Hand-Written Graphic Symbol Recognition. Graphics Recognition Recent Advances, 1941, 193–208.
Abstract: We describe a method for hand-drawn symbol recognition based on deformable template matching able to handle uncertainty and imprecision inherent to hand-drawing. Symbols are represented as a set of straight lines and their deformations as geometric transformations of these lines. Matching, however, is done over the original binary image to avoid loss of information during line detection. It is defined as an energy minimization problem, using a Bayesian framework which allows to combine fidelity to ideal shape of the symbol and flexibility to modify the symbol in order to get the best fit to the binary input image. Prior to matching, we find the best global transformation of the symbol to start the recognition process, based on the distance between symbol lines and image lines. We have applied this method to the recognition of dimensions and symbols in architectural floor plans and we show its flexibility to recognize distorted symbols.
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Josep Llados, Ernest Valveny, Gemma Sanchez and Enric Marti. 2003. A Case Study of Pattern Recognition: Symbol Recognition in Graphic Documentsa. Proceedings of Pattern Recognition in Information Systems. ICEIS Press, 1–13.
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