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Miquel Ferrer, Ernest Valveny, F. Serratosa and Horst Bunke. 2008. Exact Median Graph Computation via Graph Embedding. 12th International Workshop on Structural and Syntactic Pattern Recognition.15–24. (LNCS.)
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Jose Antonio Rodriguez, Florent Perronnin, Gemma Sanchez and Josep Llados. 2008. Unsupervised writer style adaptation for handwritten word spotting. Pattern Recognition. 19th International Conference on, IBM Best Student Paper Award..
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Alicia Fornes, Josep Llados, Gemma Sanchez and Horst Bunke. 2008. Writer Identification in Old Handwritten Music Scores. Proceedings of the 8th International Workshop on Document Analysis Systems,.347–353.
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Partha Pratim Roy, Umapada Pal and Josep Llados. 2008. Recognition of Multi-oriented Touching Characters in Graphical Documents. Computer Vision, Graphics & Image Processing, 2008. Sixth Indian Conference on,.297–304.
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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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Muhammad Muzzamil Luqman, Jean-Yves Ramel and Josep Llados. 2012. Improving Fuzzy Multilevel Graph Embedding through Feature Selection Technique. Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop. Springer Berlin Heidelberg, 243–253. (LNCS.)
Abstract: Graphs are the most powerful, expressive and convenient data structures but there is a lack of efficient computational tools and algorithms for processing them. The embedding of graphs into numeric vector spaces permits them to access the state-of-the-art computational efficient statistical models and tools. In this paper we take forward our work on explicit graph embedding and present an improvement to our earlier proposed method, named “fuzzy multilevel graph embedding – FMGE”, through feature selection technique. FMGE achieves the embedding of attributed graphs into low dimensional vector spaces by performing a multilevel analysis of graphs and extracting a set of global, structural and elementary level features. Feature selection permits FMGE to select the subset of most discriminating features and to discard the confusing ones for underlying graph dataset. Experimental results for graph classification experimentation on IAM letter, GREC and fingerprint graph databases, show improvement in the performance of FMGE.
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Muhammad Muzzamil Luqman, Thierry Brouard, Jean-Yves Ramel and Josep Llados. 2012. Recherche de sous-graphes par encapsulation floue des cliques d'ordre 2: Application à la localisation de contenu dans les images de documents graphiques. Colloque International Francophone sur l'Écrit et le Document.149–162.
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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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Antonio Clavelli and Dimosthenis Karatzas. 2009. Text Segmentation in Colour Posters from the Spanish Civil War Era. 10th International Conference on Document Analysis and Recognition.181–185.
Abstract: The extraction of textual content from colour documents of a graphical nature is a complicated task. The text can be rendered in any colour, size and orientation while the existence of complex background graphics with repetitive patterns can make its localization and segmentation extremely difficult.
Here, we propose a new method for extracting textual content from such colour images that makes no assumption as to the size of the characters, their orientation or colour, while it is tolerant to characters that do not follow a straight baseline. We evaluate this method on a collection of documents with historical
connotations: the Posters from the Spanish Civil War.
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Miquel Ferrer, Dimosthenis Karatzas, Ernest Valveny and Horst Bunke. 2009. A Recursive Embedding Approach to Median Graph Computation. 7th IAPR – TC–15 Workshop on Graph–Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 113–123. (LNCS.)
Abstract: The median graph has been shown to be a good choice to infer a representative of a set of graphs. It has been successfully applied to graph-based classification and clustering. Nevertheless, its computation is extremely complex. Several approaches have been presented up to now based on different strategies. In this paper we present a new approximate recursive algorithm for median graph computation based on graph embedding into vector spaces. Preliminary experiments on three databases show that this new approach is able to obtain better medians than the previous existing approaches.
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