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Author |
Josep Llados; Ernest Valveny; Enric Marti |
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Title  |
Symbol Recognition in Document Image Analysis: Methods and Challenges |
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2000 |
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Recent Research Developments in Pattern Recognition, Transworld Research Network, |
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1 |
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151–178. |
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81-86846-61-1 |
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DAG;IAM |
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IAM @ iam @ LVM2000 |
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1575 |
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Author |
Marçal Rusiñol; K. Bertet; Jean-Marc Ogier; Josep Llados |
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Title |
Symbol Recognition Using a Concept Lattice of Graphical Patterns |
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Conference Article |
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2009 |
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8th IAPR International Workshop on Graphics Recognition |
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In this paper we propose a new approach to recognize symbols by the use of a concept lattice. We propose to build a concept lattice in terms of graphical patterns. Each model symbol is decomposed in a set of composing graphical patterns taken as primitives. Each one of these primitives is described by boundary moment invariants. The obtained concept lattice relates which symbolic patterns compose a given graphical symbol. A Hasse diagram is derived from the context and is used to recognize symbols affected by noise. We present some preliminary results over a variation of the dataset of symbols from the GREC 2005 symbol recognition contest. |
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La Rochelle; July 2009 |
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GREC |
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DAG @ dag @ RBO2009 |
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1444 |
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Author |
Marçal Rusiñol; K. Bertet; Jean-Marc Ogier; Josep Llados |
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Title |
Symbol Recognition Using a Concept Lattice of Graphical Patterns |
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Book Chapter |
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Year |
2010 |
Publication |
Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers |
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6020 |
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187-198 |
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In this paper we propose a new approach to recognize symbols by the use of a concept lattice. We propose to build a concept lattice in terms of graphical patterns. Each model symbol is decomposed in a set of composing graphical patterns taken as primitives. Each one of these primitives is described by boundary moment invariants. The obtained concept lattice relates which symbolic patterns compose a given graphical symbol. A Hasse diagram is derived from the context and is used to recognize symbols affected by noise. We present some preliminary results over a variation of the dataset of symbols from the GREC 2005 symbol recognition contest. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-13727-3 |
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Admin @ si @ RBO2010 |
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2407 |
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Author |
Josep Llados; Gemma Sanchez |
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Title |
Symbol Recognition Using Graphs |
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Miscellaneous |
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2003 |
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Proceedings of 10th IEEE International Conference on Image Processing, vol II, 49–52 |
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Barcelona |
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DAG |
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DAG @ dag @ LlS2003 |
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383 |
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Author |
Josep Llados; Ernest Valveny; Gemma Sanchez; Enric Marti |
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Title |
Symbol recognition: current advances and perspectives |
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2002 |
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Graphics Recognition Algorithms And Applications |
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2390 |
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104-128 |
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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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London, UK |
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Springer-Verlag |
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Dorothea Blostein and Young- Bin Kwon |
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Lecture Notes in Computer Science |
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3-540-44066-6 |
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GREC |
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DAG; IAM; |
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IAM @ iam @ LVS2002 |
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1572 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Symbol Spotting in Digital Libraries:Focused Retrieval over Graphic-rich Document Collections |
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Book Whole |
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Year |
2010 |
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Symbol Spotting in Digital Libraries:Focused Retrieval over Graphic-rich Document Collections |
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Focused Retrieval , Graphical Pattern Indexation,Graphics Recognition ,Pattern Recognition , Performance Evaluation , Symbol Description ,Symbol Spotting |
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The specific problem of symbol recognition in graphical documents requires additional techniques to those developed for character recognition. The most well-known obstacle is the so-called Sayre paradox: Correct recognition requires good segmentation, yet improvement in segmentation is achieved using information provided by the recognition process. This dilemma can be avoided by techniques that identify sets of regions containing useful information. Such symbol-spotting methods allow the detection of symbols in maps or technical drawings without having to fully segment or fully recognize the entire content.
This unique text/reference provides a complete, integrated and large-scale solution to the challenge of designing a robust symbol-spotting method for collections of graphic-rich documents. The book examines a number of features and descriptors, from basic photometric descriptors commonly used in computer vision techniques to those specific to graphical shapes, presenting a methodology which can be used in a wide variety of applications. Additionally, readers are supplied with an insight into the problem of performance evaluation of spotting methods. Some very basic knowledge of pattern recognition, document image analysis and graphics recognition is assumed. |
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Springer |
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978-1-84996-208-7 |
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DAG @ dag @ RuL2010a |
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1292 |
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Author |
Anjan Dutta |
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Title |
Symbol Spotting in Graphical Documents by Serialized Subgraph Matching |
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Report |
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2010 |
Publication |
CVC Technical Report |
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159 |
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Master's thesis |
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Admin @ si @ Dut2010 |
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1351 |
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Author |
Anjan Dutta; Josep Llados; Umapada Pal |
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Title |
Symbol Spotting in Line Drawings Through Graph Paths Hashing |
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Conference Article |
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2011 |
Publication |
11th International Conference on Document Analysis and Recognition |
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982-986 |
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In this paper we propose a symbol spotting technique through hashing the shape descriptors of graph paths (Hamiltonian paths). Complex graphical structures in line drawings can be efficiently represented by graphs, which ease the accurate localization of the model symbol. Graph paths are the factorized substructures of graphs which enable robust recognition even in the presence of noise and distortion. In our framework, the entire database of the graphical documents is indexed in hash tables by the locality sensitive hashing (LSH) of shape descriptors of the paths. The hashing data structure aims to execute an approximate k-NN search in a sub-linear time. The spotting method is formulated by a spatial voting scheme to the list of locations of the paths that are decided during the hash table lookup process. We perform detailed experiments with various dataset of line drawings and the results demonstrate the effectiveness and efficiency of the technique. |
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Beijing, China |
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1520-5363 |
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978-1-4577-1350-7 |
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ICDAR |
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Admin @ si @ DLP2011b |
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1791 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Symbol Spotting in Technical Drawings Using Vectorial Signatures |
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Miscellaneous |
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2005 |
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6th IAPR International Workshop on Graphics Recognition (GREC 2005), 35–45 |
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Hong Kong |
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DAG @ dag @ RuL2005 |
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579 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Symbol Spotting in Technical Drawings Using Vectorial Signatures |
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2006 |
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Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 35–46 |
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DAG @ dag @ RuL2006a |
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719 |
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