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Alicia Fornes, Sergio Escalera, Josep Llados, Gemma Sanchez and Joan Mas. 2008. Hand Drawn Symbol Recognition by Blurred Shape Model Descriptor and a Multiclass Classifier. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities.30–40. (LNCS.)
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Alicia Fornes, Josep Llados and Gemma Sanchez. 2008. Old Handwritten Musical Symbol Classification by a Dynamic TimeWrapping Based Method. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities.52–60. (LNCS.)
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Joan Mas, J.A. Jorge, Gemma Sanchez and Josep Llados. 2008. Representing and Parsing Sketched Symbols using Adjacency Grammars and a Grid-Directed Parser. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities,.176–187. (LNCS.)
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Partha Pratim Roy, Eduard Vazquez, Josep Llados, Ramon Baldrich and Umapada Pal. 2008. A System to Segment Text and Symbols from Color Maps. Graphics Recognition. Recent Advances and New Opportunities.245–256. (LNCS.)
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Agata Lapedriza, Jaume Garcia, Ernest Valveny, Robert Benavente, Miquel Ferrer and Gemma Sanchez. 2008. Una experiencia de aprenentatge basada en projectes en el ambit de la informatica.
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Robert Benavente, Ernest Valveny, Jaume Garcia, Agata Lapedriza, Miquel Ferrer and Gemma Sanchez. 2008. Una experiencia de adaptacion al EEES de las asignaturas de programacion en Ingenieria Informatica.
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Alfons Juan-Ciscar and Gemma Sanchez. 2008. PRIS 2008. Pattern Recognition in Information Systems. Proceedings of the 8th international Workshop on Pattern Recognition in Information systems – PRIS 2008, in conjunction with ICEIS 2008.
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Josep Llados and Marçal Rusiñol. 2014. Graphics Recognition Techniques. In D. Doermann and K. Tombre, eds. Handbook of Document Image Processing and Recognition. Springer London, 489–521.
Abstract: This chapter describes the most relevant approaches for the analysis of graphical documents. The graphics recognition pipeline can be splitted into three tasks. The low level or lexical task extracts the basic units composing the document. The syntactic level is focused on the structure, i.e., how graphical entities are constructed, and involves the location and classification of the symbols present in the document. The third level is a functional or semantic level, i.e., it models what the graphical symbols do and what they mean in the context where they appear. This chapter covers the lexical level, while the next two chapters are devoted to the syntactic and semantic level, respectively. The main problems reviewed in this chapter are raster-to-vector conversion (vectorization algorithms) and the separation of text and graphics components. The research and industrial communities have provided standard methods achieving reasonable performance levels. Hence, graphics recognition techniques can be considered to be in a mature state from a scientific point of view. Additionally this chapter provides insights on some related problems, namely, the extraction and recognition of dimensions in engineering drawings, and the recognition of hatched and tiled patterns. Both problems are usually associated, even integrated, in the vectorization process.
Keywords: Dimension recognition; Graphics recognition; Graphic-rich documents; Polygonal approximation; Raster-to-vector conversion; Texture-based primitive extraction; Text-graphics separation
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Jose Antonio Rodriguez, Gemma Sanchez and Josep Llados. 2008. Categorization of Digital Ink Elements using Spectral Features. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities. Springer–Verlag, 188–198. (LNCS.)
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Miquel Ferrer, Ernest Valveny and F. Serratosa. 2009. Median graph: A new exact algorithm using a distance based on the maximum common subgraph. PRL, 30(5), 579–588.
Abstract: Median graphs have been presented as a useful tool for capturing the essential information of a set of graphs. Nevertheless, computation of optimal solutions is a very hard problem. In this work we present a new and more efficient optimal algorithm for the median graph computation. With the use of a particular cost function that permits the definition of the graph edit distance in terms of the maximum common subgraph, and a prediction function in the backtracking algorithm, we reduce the size of the search space, avoiding the evaluation of a great amount of states and still obtaining the exact median. We present a set of experiments comparing our new algorithm against the previous existing exact algorithm using synthetic data. In addition, we present the first application of the exact median graph computation to real data and we compare the results against an approximate algorithm based on genetic search. These experimental results show that our algorithm outperforms the previous existing exact algorithm and in addition show the potential applicability of the exact solutions to real problems.
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