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Josep Llados, Enric Marti and Juan J.Villanueva. 2001. Symbol recognition by error-tolerant subgraph matching between region adjacency graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1137–1143.
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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Ernest Valveny and Enric Marti. 2001. Learning of structural descriptions of graphic symbols using deformable template matching. Proc. Sixth Int Document Analysis and Recognition Conf.455–459.
Abstract: Accurate symbol recognition in graphic documents needs an accurate representation of the symbols to be recognized. If structural approaches are used for recognition, symbols have to be described in terms of their shape, using structural relationships among extracted features. Unlike statistical pattern recognition, in structural methods, symbols are usually manually defined from expertise knowledge, and not automatically infered from sample images. In this work we explain one approach to learn from examples a representative structural description of a symbol, thus providing better information about shape variability. The description of a symbol is based on a probabilistic model. It consists of a set of lines described by the mean and the variance of line parameters, respectively providing information about the model of the symbol, and its shape variability. The representation of each image in the sample set as a set of lines is achieved using deformable template matching.
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Robert Benavente, Gemma Sanchez, Ramon Baldrich, Maria Vanrell and Josep Llados. 2000. Normalized colour segmentation for human appearance description..
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Gemma Sanchez, Josep Llados and K. Tombre. 2000. A mean string algorithm to compute the average among a set of 2D shapes.
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Jaime Lopez-Krahe, Josep Llados and Enric Marti. 2000. Architectural Floor Plan Analysis. University of Edinburgh.
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Josep Llados, Jaime Lopez-Krahe, Gemma Sanchez and Enric Marti. 2000. Interprétation de cartes et plans par mise en correspondance de graphes de attributs. 12 Congrès Francophone AFRIF–AFIA.225–234.
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Josep Llados, Ernest Valveny and Enric Marti. 2000. Symbol Recognition in Document Image Analysis: Methods and Challenges. Recent Research Developments in Pattern Recognition, Transworld Research Network,, 1, 151–178.
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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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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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Josep Llados, J. Lopez-Krahe and Enric Marti. 1999. A Hough-based method for hatched pattern detection in maps and diagrams..
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