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Author Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Bemedi edit   pdf
doi  isbn
openurl 
  Title Page Segmentation of Structured Documents Using 2D Stochastic Context-Free Grammars Type Conference Article
  Year 2013 Publication 6th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume (down) 7887 Issue Pages 133-140  
  Keywords  
  Abstract In this paper we define a bidimensional extension of Stochastic Context-Free Grammars for page segmentation of structured documents. Two sets of text classification features are used to perform an initial classification of each zone of the page. Then, the page segmentation is obtained as the most likely hypothesis according to a grammar. This approach is compared to Conditional Random Fields and results show significant improvements in several cases. Furthermore, grammars provide a detailed segmentation that allowed a semantic evaluation which also validates this model.  
  Address Madeira; Portugal; June 2013  
  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 978-3-642-38627-5 Medium  
  Area Expedition Conference IbPRIA  
  Notes DAG; 605.203 Approved no  
  Call Number Admin @ si @ ACS2013 Serial 2328  
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Author Antonio Clavelli; Dimosthenis Karatzas; Josep Llados; Mario Ferraro; Giuseppe Boccignone edit   pdf
url  doi
isbn  openurl
  Title Towards Modelling an Attention-Based Text Localization Process Type Conference Article
  Year 2013 Publication 6th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume (down) 7887 Issue Pages 296-303  
  Keywords text localization; visual attention; eye guidance  
  Abstract This note introduces a visual attention model of text localization in real-world scenes. The core of the model built upon the proto-object concept is discussed. It is shown how such dynamic mid-level representation of the scene can be derived in the framework of an action-perception loop engaging salience, text information value computation, and eye guidance mechanisms.
Preliminary results that compare model generated scanpaths with those eye-tracked from human subjects are presented.
 
  Address Madeira; Portugal; June 2013  
  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 978-3-642-38627-5 Medium  
  Area Expedition Conference IbPRIA  
  Notes DAG Approved no  
  Call Number Admin @ si @ CKL2013 Serial 2291  
Permanent link to this record
 

 
Author Nuria Cirera; Alicia Fornes; Volkmar Frinken; Josep Llados edit   pdf
doi  isbn
openurl 
  Title Hybrid grammar language model for handwritten historical documents recognition Type Conference Article
  Year 2013 Publication 6th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume (down) 7887 Issue Pages 117-124  
  Keywords  
  Abstract In this paper we present a hybrid language model for the recognition of handwritten historical documents with a structured syntactical layout. Using a hidden Markov model-based recognition framework, a word-based grammar with a closed dictionary is enhanced by a character sequence recognition method. This allows to recognize out-of-dictionary words in controlled parts of the recognition, while keeping a closed vocabulary restriction for other parts. While the current status is work in progress, we can report an improvement in terms of character error rate.  
  Address Madeira; Portugal; June 2013  
  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 978-3-642-38627-5 Medium  
  Area Expedition Conference IbPRIA  
  Notes DAG; 602.006; 600.045; 600.061 Approved no  
  Call Number Admin @ si @ CFF2013 Serial 2292  
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Author Andreas Fischer; Ching Y. Suen; Volkmar Frinken; Kaspar Riesen; Horst Bunke edit   pdf
doi  isbn
openurl 
  Title A Fast Matching Algorithm for Graph-Based Handwriting Recognition Type Conference Article
  Year 2013 Publication 9th IAPR – TC15 Workshop on Graph-based Representation in Pattern Recognition Abbreviated Journal  
  Volume (down) 7877 Issue Pages 194-203  
  Keywords  
  Abstract The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy.  
  Address Vienna; Austria; May 2013  
  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 978-3-642-38220-8 Medium  
  Area Expedition Conference GBR  
  Notes DAG; 600.045; 605.203 Approved no  
  Call Number Admin @ si @ FSF2013 Serial 2294  
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Author Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados edit  doi
isbn  openurl
  Title Improving Fuzzy Multilevel Graph Embedding through Feature Selection Technique Type Conference Article
  Year 2012 Publication Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop Abbreviated Journal  
  Volume (down) 7626 Issue Pages 243-253  
  Keywords  
  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.  
  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 978-3-642-34165-6 Medium  
  Area Expedition Conference SSPR&SPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ LRL2012 Serial 2381  
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Author Volkmar Frinken; Alicia Fornes; Josep Llados; Jean-Marc Ogier edit   pdf
doi  isbn
openurl 
  Title Bidirectional Language Model for Handwriting Recognition Type Conference Article
  Year 2012 Publication Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop Abbreviated Journal  
  Volume (down) 7626 Issue Pages 611-619  
  Keywords  
  Abstract In order to improve the results of automatically recognized handwritten text, information about the language is commonly included in the recognition process. A common approach is to represent a text line as a sequence. It is processed in one direction and the language information via n-grams is directly included in the decoding. This approach, however, only uses context on one side to estimate a word’s probability. Therefore, we propose a bidirectional recognition in this paper, using distinct forward and a backward language models. By combining decoding hypotheses from both directions, we achieve a significant increase in recognition accuracy for the off-line writer independent handwriting recognition task. Both language models are of the same type and can be estimated on the same corpus. Hence, the increase in recognition accuracy comes without any additional need for training data or language modeling complexity.  
  Address Japan  
  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 978-3-642-34165-6 Medium  
  Area Expedition Conference SSPR&SPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ FFL2012 Serial 2057  
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Author Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados edit   pdf
doi  isbn
openurl 
  Title Hierarchical graph representation for symbol spotting in graphical document images Type Conference Article
  Year 2012 Publication Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop Abbreviated Journal  
  Volume (down) 7626 Issue Pages 529-538  
  Keywords  
  Abstract Symbol spotting can be defined as locating given query symbol in a large collection of graphical documents. In this paper we present a hierarchical graph representation for symbols. This representation allows graph matching methods to deal with low-level vectorization errors and, thus, to perform a robust symbol spotting. To show the potential of this approach, we conduct an experiment with the SESYD dataset.  
  Address Miyajima-Itsukushima, Hiroshima  
  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 978-3-642-34165-6 Medium  
  Area Expedition Conference SSPR&SPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ BDJ2012 Serial 2126  
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Author Jaume Gibert; Ernest Valveny; Horst Bunke; Alicia Fornes edit   pdf
doi  isbn
openurl 
  Title On the Correlation of Graph Edit Distance and L1 Distance in the Attribute Statistics Embedding Space Type Conference Article
  Year 2012 Publication Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop Abbreviated Journal  
  Volume (down) 7626 Issue Pages 135-143  
  Keywords  
  Abstract Graph embeddings in vector spaces aim at assigning a pattern vector to every graph so that the problems of graph classification and clustering can be solved by using data processing algorithms originally developed for statistical feature vectors. An important requirement graph features should fulfil is that they reproduce as much as possible the properties among objects in the graph domain. In particular, it is usually desired that distances between pairs of graphs in the graph domain closely resemble those between their corresponding vectorial representations. In this work, we analyse relations between the edit distance in the graph domain and the L1 distance of the attribute statistics based embedding, for which good classification performance has been reported on various datasets. We show that there is actually a high correlation between the two kinds of distances provided that the corresponding parameter values that account for balancing the weight between node and edge based features are properly selected.  
  Address  
  Corporate Author Thesis  
  Publisher Springer-Berlag, Berlin Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-642-34165-6 Medium  
  Area Expedition Conference SSPR&SPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ GVB2012c Serial 2167  
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Author Oriol Ramos Terrades; N. Serrano; Albert Gordo; Ernest Valveny; Alfons Juan-Ciscar edit  doi
openurl 
  Title Interactive-predictive detection of handwritten text blocks Type Conference Article
  Year 2010 Publication 17th Document Recognition and Retrieval Conference, part of the IS&T-SPIE Electronic Imaging Symposium Abbreviated Journal  
  Volume (down) 7534 Issue Pages 75340Q–75340Q–10  
  Keywords  
  Abstract A method for text block detection is introduced for old handwritten documents. The proposed method takes advantage of sequential book structure, taking into account layout information from pages previously transcribed. This glance at the past is used to predict the position of text blocks in the current page with the help of conventional layout analysis methods. The method is integrated into the GIDOC prototype: a first attempt to provide integrated support for interactive-predictive page layout analysis, text line detection and handwritten text transcription. Results are given in a transcription task on a 764-page Spanish manuscript from 1891.  
  Address  
  Corporate Author Thesis  
  Publisher 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 DRR  
  Notes DAG Approved no  
  Call Number DAG @ dag @ TSG2010 Serial 1479  
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Author Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny edit   pdf
doi  isbn
openurl 
  Title Notation-invariant patch-based wall detector in architectural floor plans Type Book Chapter
  Year 2013 Publication Graphics Recognition. New Trends and Challenges Abbreviated Journal  
  Volume (down) 7423 Issue Pages 79--88  
  Keywords  
  Abstract Architectural floor plans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed in [1], showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. This paper presents an evolution of that previous work, after analyzing and testing several alternatives for each of the different steps of the method: Firstly, an automatic plan-size normalization process is done. Secondly we evaluate different features to obtain the description of every patch. Thirdly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These variations of the method have been tested for wall detection on two datasets of architectural floor plans with different notations. After studying in deep each of the steps in the process pipeline, we are able to find the best system configuration, which highly outperforms the results on wall segmentation obtained by the original paper.  
  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 978-3-642-36823-3 Medium  
  Area Expedition Conference  
  Notes DAG; 600.045; 600.056; 605.203 Approved no  
  Call Number Admin @ si @ HMS2013 Serial 2322  
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