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Jaime Lopez-Krahe, Josep Llados, & Enric Marti. (2000). "Architectural Floor Plan Analysis " (Robert B. Fisher, Ed.). University of Edinburgh.
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Josep Llados, Jaime Lopez-Krahe, Gemma Sanchez, & Enric Marti. (2000)." Interprétation de cartes et plans par mise en correspondance de graphes de attributs" In 12 Congrès Francophone AFRIF–AFIA (Vol. 3, pp. 225–234).
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Josep Llados, Ernest Valveny, & Enric Marti. (2000)." Symbol Recognition in Document Image Analysis: Methods and Challenges" In Recent Research Developments in Pattern Recognition, Transworld Research Network, (Vol. 1, 151–178.).
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Ernest Valveny, & 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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Ernest Valveny, & Enric Marti. (2000). "Hand-drawn symbol recognition in graphic documents using deformable template matching and a Bayesian framework " In Proc. 15th Int Pattern Recognition Conf (Vol. 2, pp. 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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Josep Llados, J. Lopez-Krahe, & Enric Marti. (1999)." A Hough-based method for hatched pattern detection in maps and diagrams." .
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Josep Llados, & Enric Marti. (1999)." A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems." .
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Josep Llados, & Enric Marti. (1999)." A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems" . Machine Graphics & Vision, 8, 195–211.
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Josep Llados, & Enric Marti. (1999)." Graph-edit algorithms for hand-drawn graphical document recognition and their automatic introduction" . Machine Graphics & Vision journal, special issue on Graph transformation, .
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Josep Llados, Enric Marti, & Jaime Lopez-Krahe. (1999). "A Hough-based method for hatched pattern detection in maps and diagrams " In Proceeding of the Fifth Int. Conf. Document Analysis and Recognition ICDAR ’99 (pp. 479–482).
Abstract: A hatched area is characterized by a set of parallel straight lines placed at regular intervals. In this paper, a Hough-based schema is introduced to recognize hatched areas in technical documents from attributed graph structures representing the document once it has been vectorized. Defining a Hough-based transform from a graph instead of the raster image allows to drastically reduce the processing time and, second, to obtain more reliable results because straight lines have already been detected in the vectorization step. A second advantage of the proposed method is that no assumptions must be made a priori about the slope and frequency of hatching patterns, but they are computed in run time for each hatched area.
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