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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  Title Hybrid grammar language model for handwritten historical documents recognition Type Conference Article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 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  Abstract In this paper we present a hybrid language model for the recognition of handwritten historical documents with a structured syntactical layout.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 (up) IbPRIA  
  Notes DAG; 602.006; 600.045; 600.061 Approved no  
  Call Number Admin @ si @ CFF2013 Serial 2292  
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Author Suman Ghosh; Ernest Valveny edit   pdf
doi  isbn
openurl 
  Title A Sliding Window Framework for Word Spotting Based on Word Attributes Type Conference Article
  Year 2015 Publication Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 Abbreviated Journal  
  Volume 9117 Issue Pages 652-661  
  Keywords Word spotting; Sliding window; Word attributes  
  Abstract In this paper we propose a segmentation-free approach to word spotting. Word images are first encoded into feature vectors using Fisher Vector. Then, these feature vectors are used together with pyramidal histogram of characters labels (PHOC) to learn SVM-based attribute models. Documents are represented by these PHOC based word attributes. To efficiently compute the word attributes over a sliding window, we propose to use an integral image representation of the document using a simplified version of the attribute model. Finally we re-rank the top word candidates using the more discriminative full version of the word attributes. We show state-of-the-art results for segmentation-free query-by-example word spotting in single-writer and multi-writer standard datasets.  
  Address Santiago de Compostela; June 2015  
  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 0302-9743 ISBN 978-3-319-19389-2 Medium  
  Area Expedition Conference (up) IbPRIA  
  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ GhV2015b Serial 2716  
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Author Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez edit   pdf
isbn  openurl
  Title Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology Type Conference Article
  Year 2017 Publication 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume 10255 Issue Pages 287-294  
  Keywords Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model  
  Abstract Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach.  
  Address Faro; Portugal; June 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor L.A. Alexandre; J.Salvador Sanchez; Joao M. F. Rodriguez  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-319-58837-7 Medium  
  Area Expedition Conference (up) IbPRIA  
  Notes DAG; 602.006; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ RFV2017 Serial 2952  
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Author Hana Jarraya; Oriol Ramos Terrades; Josep Llados edit   pdf
url  openurl
  Title Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs Type Conference Article
  Year 2017 Publication 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume Issue Pages  
  Keywords Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines  
  Abstract We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction.  
  Address Faro; Portugal; June 2017  
  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 (up) IbPRIA  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ JRL2017a Serial 2953  
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Author Oriol Ramos Terrades; Salvatore Tabbone; Ernest Valveny edit  openurl
  Title Optimal Linear Combination for Two-class Classifiers Type Conference Article
  Year 2007 Publication Proceedings of the International Conference on Advances in Pattern Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Kolkata (India)  
  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 (up) ICAPR  
  Notes DAG Approved no  
  Call Number DAG @ dag @ RTV2007a Serial 894  
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Author Partha Pratim Roy; Josep Llados edit  openurl
  Title Multi-Oriented Character Recognition from Graphical Documents Type Conference Article
  Year 2008 Publication 2nd International Conference on Cognition and Recognition Abbreviated Journal  
  Volume Issue Pages 30–35  
  Keywords  
  Abstract  
  Address Mandya (India)  
  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 (up) ICCR  
  Notes DAG Approved no  
  Call Number DAG @ dag @ RLP2008 Serial 965  
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Author Partha Pratim Roy; Josep Llados; Umapada Pal edit  openurl
  Title Text/Graphics Separation in Color Maps Type Conference Article
  Year 2007 Publication International Conference on Computing: Theory and Applications Abbreviated Journal  
  Volume Issue Pages 545–551  
  Keywords  
  Abstract  
  Address Kolkata (India)  
  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 (up) ICCTA  
  Notes DAG Approved no  
  Call Number DAG @ dag @ RLP2007a Serial 806  
Permanent link to this record
 

 
Author Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny edit   pdf
doi  openurl
  Title Handwritten Word Spotting with Corrected Attributes Type Conference Article
  Year 2013 Publication 15th IEEE International Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages 1017-1024  
  Keywords  
  Abstract We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results.  
  Address Sydney; Australia; December 2013  
  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 1550-5499 ISBN Medium  
  Area Expedition Conference (up) ICCV  
  Notes DAG Approved no  
  Call Number Admin @ si @ AGF2013 Serial 2327  
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Author Ali Furkan Biten; R. Tito; Andres Mafla; Lluis Gomez; Marçal Rusiñol; C.V. Jawahar; Ernest Valveny; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Scene Text Visual Question Answering Type Conference Article
  Year 2019 Publication 18th IEEE International Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages 4291-4301  
  Keywords  
  Abstract Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims to highlight the importance of exploiting highlevel semantic information present in images as textual cues in the Visual Question Answering process. We use this dataset to define a series of tasks of increasing difficulty for which reading the scene text in the context provided by the visual information is necessary to reason and generate an appropriate answer. We propose a new evaluation metric for these tasks to account both for reasoning errors as well as shortcomings of the text recognition module. In addition we put forward a series of baseline methods, which provide further insight to the newly released dataset, and set the scene for further research.  
  Address Seul; Corea; October 2019  
  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 (up) ICCV  
  Notes DAG; 600.129; 600.135; 601.338; 600.121 Approved no  
  Call Number Admin @ si @ BTM2019b Serial 3285  
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Author Jordy Van Landeghem; Ruben Tito; Lukasz Borchmann; Michal Pietruszka; Pawel Joziak; Rafal Powalski; Dawid Jurkiewicz; Mickael Coustaty; Bertrand Anckaert; Ernest Valveny; Matthew Blaschko; Sien Moens; Tomasz Stanislawek edit   pdf
url  openurl
  Title Document Understanding Dataset and Evaluation (DUDE) Type Conference Article
  Year 2023 Publication 20th IEEE International Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages 19528-19540  
  Keywords  
  Abstract We call on the Document AI (DocAI) community to re-evaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks. Document Understanding Dataset and Evaluation (DUDE) seeks to remediate the halted research progress in understanding visually-rich documents (VRDs). We present a new dataset with novelties related to types of questions, answers, and document layouts based on multi-industry, multi-domain, and multi-page VRDs of various origins and dates. Moreover, we are pushing the boundaries of current methods by creating multi-task and multi-domain evaluation setups that more accurately simulate real-world situations where powerful generalization and adaptation under low-resource settings are desired. DUDE aims to set a new standard as a more practical, long-standing benchmark for the community, and we hope that it will lead to future extensions and contributions that address real-world challenges. Finally, our work illustrates the importance of finding more efficient ways to model language, images, and layout in DocAI.  
  Address Paris; France; October 2023  
  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 (up) ICCV  
  Notes DAG Approved no  
  Call Number Admin @ si @ LTB2023 Serial 3948  
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