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Oriol Ramos Terrades and Ernest Valveny. 2004. Indexing Technical Symbols Using Ridgelets Transform.
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Ernest Valveny and Philippe Dosch. 2004. Symbol Recognition Contest: A Synthesis.
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Gemma Sanchez and Josep Llados. 2004. Syntactic models to represent perceptually regular repetitive patterns in graphic documents.
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Joan Mas, Gemma Sanchez and Josep Llados. 2006. An Incremental Parser to Recognize Diagram Symbols and Gestures represented by Adjacency Grammars.
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Alicia Fornes, Josep Llados and Gemma Sanchez. 2006. Primitive Segmentation in Old Handwritten Music Scores. Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 288–299.
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Marçal Rusiñol and Josep Llados. 2006. Symbol Spotting in Technical Drawings Using Vectorial Signatures. Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 35–46.
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Philippe Dosch and Ernest Valveny. 2006. Report on the Second Symbol Recognition Contest. Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 381–397.
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Mathieu Nicolas Delalandre, Jean-Marc Ogier and Josep Llados. 2008. A Fast Cbir System of Old Ornamental Letter. In W. Liu, J.L., J.M. Ogier, ed. Graphics Reognition: Recent Advances and New Opportunities.135–144. (LNCS.)
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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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Salvatore Tabbone and Oriol Ramos Terrades. 2014. An Overview of Symbol Recognition. In D. Doermann and K. Tombre, eds. Handbook of Document Image Processing and Recognition. Springer London, 523–551.
Abstract: According to the Cambridge Dictionaries Online, a symbol is a sign, shape, or object that is used to represent something else. Symbol recognition is a subfield of general pattern recognition problems that focuses on identifying, detecting, and recognizing symbols in technical drawings, maps, or miscellaneous documents such as logos and musical scores. This chapter aims at providing the reader an overview of the different existing ways of describing and recognizing symbols and how the field has evolved to attain a certain degree of maturity.
Keywords: Pattern recognition; Shape descriptors; Structural descriptors; Symbolrecognition; Symbol spotting
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