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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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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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Book Chapter |
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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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Author |
Marçal Rusiñol; Josep Llados |
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Title |
A Region-Based Hashing Approach for Symbol Spotting in Thechnical Documents |
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Conference Article |
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2007 |
Publication |
Seventh IAPR International Workshop on Graphics Recognition |
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41–42 |
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Curitiba (Brazil) |
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J. Llados, W. Liu, J.M. Ogier |
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GREC |
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DAG @ dag @ RuL2007a |
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846 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
A Region-Based Hashing Approach for Symbol Spotting in Technical Documents |
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2008 |
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Graphics Recognition: Recent Advances and New Opportunities |
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5046 |
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104–113 |
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W. Lius, J. Llados, J.M. Ogier |
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LNCS |
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DAG @ dag @ RuL2008a |
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959 |
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Marçal Rusiñol; Josep Llados |
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Title |
Word and Symbol Spotting using Spatial Organization of Local Descriptors |
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Conference Article |
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2008 |
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Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, |
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489–496 |
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Nara (Japan) |
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DAG @ dag @ RuL2008b |
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1059 |
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Marçal Rusiñol; Josep Llados |
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Title |
A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices |
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Journal Article |
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2009 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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12 |
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2 |
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83-96 |
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Performance evaluation; Symbol Spotting; Graphics Recognition |
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Symbol spotting systems are intended to retrieve regions of interest from a document image database where the queried symbol is likely to be found. They shall have the ability to recognize and locate graphical symbols in a single step. In this paper, we present a set of measures to evaluate the performance of a symbol spotting system in terms of recognition abilities, location accuracy and scalability. We show that the proposed measures allow to determine the weaknesses and strengths of different methods. In particular we have tested a symbol spotting method based on a set of four different off-the-shelf shape descriptors. |
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1433-2833 |
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DAG @ dag @ RuL2009a |
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1166 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Logo Spotting by a Bag-of-words Approach for Document Categorization |
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Conference Article |
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2009 |
Publication |
10th International Conference on Document Analysis and Recognition |
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111–115 |
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In this paper we present a method for document categorization which processes incoming document images such as invoices or receipts. The categorization of these document images is done in terms of the presence of a certain graphical logo detected without segmentation. The graphical logos are described by a set of local features and the categorization of the documents is performed by the use of a bag-of-words model. Spatial coherence rules are added to reinforce the correct category hypothesis, aiming also to spot the logo inside the document image. Experiments which demonstrate the effectiveness of this system on a large set of real data are presented. |
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Barcelona; Spain |
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1520-5363 |
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978-1-4244-4500-4 |
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ICDAR |
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DAG @ dag @ RuL2009b |
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1179 |
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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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2010 |
Publication |
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 |
Marçal Rusiñol; Josep Llados |
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Title |
Efficient Logo Retrieval Through Hashing Shape Context Descriptors |
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Conference Article |
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2010 |
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9th IAPR International Workshop on Document Analysis Systems |
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215–222 |
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In this paper, we present an approach towards the retrieval of words from graphical document images. In graphical documents, due to presence of multi-oriented characters in non-structured layout, word indexing is a challenging task. The proposed approach uses recognition results of individual components to form character pairs with the neighboring components. An indexing scheme is designed to store the spatial description of components and to access them efficiently. Given a query text word (ascii/unicode format), the character pairs present in it are searched in the document. Next the retrieved character pairs are linked sequentially to form character string. Dynamic programming is applied to find different instances of query words. A string edit distance is used here to match the query word as the objective function. Recognition of multi-scale and multi-oriented character component is done using Support Vector Machine classifier. To consider multi-oriented character strings the features used in the SVM are invariant to character orientation. Experimental results show that the method is efficient to locate a query word from multi-oriented text in graphical documents. |
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Boston; USA |
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DAG @ dag @ RuL2010b |
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1434 |
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Marçal Rusiñol |
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A Model of Vectorial Signatures in Terms of Expressive Sub-Shapes: Symbol Indexation in Technical Documents |
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2006 |
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CVC Technical Report #94 |
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CVC (UAB) |
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