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Author Mathieu Nicolas Delalandre; Jean-Yves Ramel; Ernest Valveny; Muhammad Muzzamil Luqman edit  doi
isbn  openurl
  Title A Performance Characterization Algorithm for Symbol Localization Type Book Chapter
  Year 2010 Publication (down) Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers Abbreviated Journal  
  Volume 6020 Issue Pages 260–271  
  Keywords  
  Abstract In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm is aimed to be a more “reliable” and “open” solution to characterize the performance. To achieve that, it exploits only single points as the result of localization and offers the possibility to reconsider the localization results provided by a system. We use the information about context in groundtruth, and overall localization results, to detect the ambiguous localization results. A probability score is computed for each matching between a localization point and a groundtruth region, depending on the spatial distribution of the other regions in the groundtruth. Final characterization is given with detection rate/probability score plots, describing the sets of possible interpretations of the localization results, according to a given confidence rate. We present experimentation details along with the results for the symbol localization system of [1], exploiting a synthetic dataset of architectural floorplans and electrical diagrams (composed of 200 images and 3861 symbols).  
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-13727-3 Medium  
  Area Expedition Conference GREC  
  Notes DAG Approved no  
  Call Number Admin @ si @ DRV2010 Serial 2406  
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Author Marçal Rusiñol; K. Bertet; Jean-Marc Ogier; Josep Llados edit  doi
isbn  openurl
  Title Symbol Recognition Using a Concept Lattice of Graphical Patterns Type Book Chapter
  Year 2010 Publication (down) Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers Abbreviated Journal  
  Volume 6020 Issue Pages 187-198  
  Keywords  
  Abstract 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.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-13727-3 Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ RBO2010 Serial 2407  
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Author Partha Pratim Roy; Umapada Pal; Josep Llados edit  doi
isbn  openurl
  Title Touching Text Character Localization in Graphical Documents using SIFT Type Book Chapter
  Year 2010 Publication (down) Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers Abbreviated Journal  
  Volume 6020 Issue Pages 199-211  
  Keywords Support Vector Machine; Text Component; Graphical Line; Document Image; Scale Invariant Feature Transform  
  Abstract Interpretation of graphical document images is a challenging task as it requires proper understanding of text/graphics symbols present in such documents. Difficulties arise in graphical document recognition when text and symbol overlapped/touched. Intersection of text and symbols with graphical lines and curves occur frequently in graphical documents and hence separation of such symbols is very difficult.
Several pattern recognition and classification techniques exist to recognize isolated text/symbol. But, the touching/overlapping text and symbol recognition has not yet been dealt successfully. An interesting technique, Scale Invariant Feature Transform (SIFT), originally devised for object recognition can take care of overlapping problems. Even if SIFT features have emerged as a very powerful object descriptors, their employment in graphical documents context has not been investigated much. In this paper we present the adaptation of the SIFT approach in the context of text character localization (spotting) in graphical documents. We evaluate the applicability of this technique in such documents and discuss the scope of improvement by combining some state-of-the-art approaches.
 
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-13727-3 Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ RPL2010c Serial 2408  
Permanent link to this record
 

 
Author Alicia Fornes; Bart Lamiroy edit  url
isbn  openurl
  Title Graphics Recognition, Current Trends and Evolutions Type Book Whole
  Year 2018 Publication (down) Graphics Recognition, Current Trends and Evolutions Abbreviated Journal  
  Volume 11009 Issue Pages  
  Keywords  
  Abstract This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Workshop on Graphics Recognition, GREC 2017, held in Kyoto, Japan, in November 2017.
The 10 revised full papers presented were carefully reviewed and selected from 14 initial submissions. They contain both classical and emerging topics of graphics rcognition, namely analysis and detection of diagrams, search and classification, optical music recognition, interpretation of engineering drawings and maps.
 
  Address  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-030-02283-9 Medium  
  Area Expedition Conference  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ FoL2018 Serial 3171  
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Author Ernest Valveny; Enric Marti edit   pdf
doi  openurl
  Title Deformable Template Matching within a Bayesian Framework for Hand-Written Graphic Symbol Recognition Type Journal Article
  Year 2000 Publication (down) Graphics Recognition Recent Advances Abbreviated Journal  
  Volume 1941 Issue Pages 193-208  
  Keywords  
  Abstract We describe a method for hand-drawn symbol recognition based on deformable template matching able to handle uncertainty and imprecision inherent to hand-drawing. Symbols are represented as a set of straight lines and their deformations as geometric transformations of these lines. Matching, however, is done over the original binary image to avoid loss of information during line detection. It is defined as an energy minimization problem, using a Bayesian framework which allows to combine fidelity to ideal shape of the symbol and flexibility to modify the symbol in order to get the best fit to the binary input image. Prior to matching, we find the best global transformation of the symbol to start the recognition process, based on the distance between symbol lines and image lines. We have applied this method to the recognition of dimensions and symbols in architectural floor plans and we show its flexibility to recognize distorted symbols.  
  Address  
  Corporate Author Springer Verlag Thesis  
  Publisher Springer Verlag Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG;IAM; Approved no  
  Call Number IAM @ iam @ MVA2000 Serial 1655  
Permanent link to this record
 

 
Author Josep Llados; Gemma Sanchez; Enric Marti edit   pdf
doi  openurl
  Title A string based method to recognize symbols and structural textures in architectural plans Type Book Chapter
  Year 1998 Publication (down) Graphics Recognition Algorithms and Systems Second International Workshop, GREC' 97 Nancy, France, August 22–23, 1997 Selected Papers Abbreviated Journal LNCS  
  Volume 1389 Issue 1998 Pages 91-103  
  Keywords  
  Abstract This paper deals with the recognition of symbols and structural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clustering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the similarity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Link Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title LNCS Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; IAM Approved no  
  Call Number IAM @ iam @ SLE1998 Serial 1573  
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Author Josep Llados; Ernest Valveny; Gemma Sanchez; Enric Marti edit   pdf
url  doi
isbn  openurl
  Title Symbol recognition: current advances and perspectives Type Book Chapter
  Year 2002 Publication (down) Graphics Recognition Algorithms And Applications Abbreviated Journal LNCS  
  Volume 2390 Issue Pages 104-128  
  Keywords  
  Abstract 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.  
  Address London, UK  
  Corporate Author Thesis  
  Publisher Springer-Verlag Place of Publication Editor Dorothea Blostein and Young- Bin Kwon  
  Language Summary Language Original Title  
  Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 3-540-44066-6 Medium  
  Area Expedition Conference GREC  
  Notes DAG; IAM; Approved no  
  Call Number IAM @ iam @ LVS2002 Serial 1572  
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Author Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados edit  url
doi  isbn
openurl 
  Title Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces Type Book Chapter
  Year 2013 Publication (down) Graph Embedding for Pattern Analysis Abbreviated Journal  
  Volume Issue Pages 1-26  
  Keywords  
  Abstract Ability to recognize patterns is among the most crucial capabilities of human beings for their survival, which enables them to employ their sophisticated neural and cognitive systems [1], for processing complex audio, visual, smell, touch, and taste signals. Man is the most complex and the best existing system of pattern recognition. Without any explicit thinking, we continuously compare, classify, and identify huge amount of signal data everyday [2], starting from the time we get up in the morning till the last second we fall asleep. This includes recognizing the face of a friend in a crowd, a spoken word embedded in noise, the proper key to lock the door, smell of coffee, the voice of a favorite singer, the recognition of alphabetic characters, and millions of more tasks that we perform on regular basis.  
  Address  
  Corporate Author Thesis  
  Publisher Springer New York Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-4614-4456-5 Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ LRL2013b Serial 2271  
Permanent link to this record
 

 
Author Miquel Ferrer; I. Bardaji; Ernest Valveny; Dimosthenis Karatzas; Horst Bunke edit  doi
isbn  openurl
  Title Median Graph Computation by Means of Graph Embedding into Vector Spaces Type Book Chapter
  Year 2013 Publication (down) Graph Embedding for Pattern Analysis Abbreviated Journal  
  Volume Issue Pages 45-72  
  Keywords  
  Abstract In pattern recognition [8, 14], a key issue to be addressed when designing a system is how to represent input patterns. Feature vectors is a common option. That is, a set of numerical features describing relevant properties of the pattern are computed and arranged in a vector form. The main advantages of this kind of representation are computational simplicity and a well sound mathematical foundation. Thus, a large number of operations are available to work with vectors and a large repository of algorithms for pattern analysis and classification exist. However, the simple structure of feature vectors might not be the best option for complex patterns where nonnumerical features or relations between different parts of the pattern become relevant.  
  Address  
  Corporate Author Thesis  
  Publisher Springer New York Place of Publication Editor Yun Fu; Yungian Ma  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-4614-4456-5 Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ FBV2013 Serial 2421  
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Author Giuseppe De Gregorio; Sanket Biswas; Mohamed Ali Souibgui; Asma Bensalah; Josep Llados; Alicia Fornes; Angelo Marcelli edit   pdf
doi  openurl
  Title A Few Shot Multi-representation Approach for N-Gram Spotting in Historical Manuscripts Type Conference Article
  Year 2022 Publication (down) Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) Abbreviated Journal  
  Volume 13639 Issue Pages 3-12  
  Keywords N-gram spotting; Few-shot learning; Multimodal understanding; Historical handwritten collections  
  Abstract Despite recent advances in automatic text recognition, the performance remains moderate when it comes to historical manuscripts. This is mainly because of the scarcity of available labelled data to train the data-hungry Handwritten Text Recognition (HTR) models. The Keyword Spotting System (KWS) provides a valid alternative to HTR due to the reduction in error rate, but it is usually limited to a closed reference vocabulary. In this paper, we propose a few-shot learning paradigm for spotting sequences of a few characters (N-gram) that requires a small amount of labelled training data. We exhibit that recognition of important n-grams could reduce the system’s dependency on vocabulary. In this case, an out-of-vocabulary (OOV) word in an input handwritten line image could be a sequence of n-grams that belong to the lexicon. An extensive experimental evaluation of our proposed multi-representation approach was carried out on a subset of Bentham’s historical manuscript collections to obtain some really promising results in this direction.  
  Address December 04 – 07, 2022; Hyderabad, India  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICFHR  
  Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ GBS2022 Serial 3733  
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