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Author Dimosthenis Karatzas;Ch. Lioutas edit  openurl
  Title Software Package Development for Electron Diffraction Image Analysis Type Conference Article
  Year 1998 Publication Proceedings of the XIV Solid State Physics National Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Ioannina, Greece  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number IAM @ iam @ KaL1998 Serial 2045  
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Author Albert Gordo; Florent Perronnin; Ernest Valveny edit   pdf
doi  isbn
openurl 
  Title Document classification using multiple views Type Conference Article
  Year 2012 Publication 10th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 33-37  
  Keywords  
  Abstract The combination of multiple features or views when representing documents or other kinds of objects usually leads to improved results in classification (and retrieval) tasks. Most systems assume that those views will be available both at training and test time. However, some views may be too `expensive' to be available at test time. In this paper, we consider the use of Canonical Correlation Analysis to leverage `expensive' views that are available only at training time. Experimental results show that this information may significantly improve the results in a classification task.  
  Address Australia  
  Corporate Author Thesis  
  Publisher IEEE Computer Society Washington Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-0-7695-4661-2 Medium  
  Area Expedition Conference DAS  
  Notes DAG Approved no  
  Call Number Admin @ si @ GPV2012 Serial 2049  
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Author Albert Gordo; Jose Antonio Rodriguez; Florent Perronnin; Ernest Valveny edit   pdf
doi  isbn
openurl 
  Title Leveraging category-level labels for instance-level image retrieval Type Conference Article
  Year 2012 Publication 25th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 3045-3052  
  Keywords  
  Abstract In this article, we focus on the problem of large-scale instance-level image retrieval. For efficiency reasons, it is common to represent an image by a fixed-length descriptor which is subsequently encoded into a small number of bits. We note that most encoding techniques include an unsupervised dimensionality reduction step. Our goal in this work is to learn a better subspace in a supervised manner. We especially raise the following question: “can category-level labels be used to learn such a subspace?” To answer this question, we experiment with four learning techniques: the first one is based on a metric learning framework, the second one on attribute representations, the third one on Canonical Correlation Analysis (CCA) and the fourth one on Joint Subspace and Classifier Learning (JSCL). While the first three approaches have been applied in the past to the image retrieval problem, we believe we are the first to show the usefulness of JSCL in this context. In our experiments, we use ImageNet as a source of category-level labels and report retrieval results on two standard dataseis: INRIA Holidays and the University of Kentucky benchmark. Our experimental study shows that metric learning and attributes do not lead to any significant improvement in retrieval accuracy, as opposed to CCA and JSCL. As an example, we report on Holidays an increase in accuracy from 39.3% to 48.6% with 32-dimensional representations. Overall JSCL is shown to yield the best results.  
  Address Providence, Rhode Island  
  Corporate Author Thesis  
  Publisher IEEE Xplore Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 1063-6919 ISBN 978-1-4673-1226-4 Medium  
  Area Expedition Conference CVPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ GRP2012 Serial 2050  
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Author Francisco Cruz; Oriol Ramos Terrades edit   pdf
url  openurl
  Title Document segmentation using relative location features Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 1562-1565  
  Keywords  
  Abstract In this paper we evaluate the use of Relative Location Features (RLF) on a historical document segmentation task, and compare the quality of the results obtained on structured and unstructured documents using RLF and not using them. We prove that using these features improve the final segmentation on documents with a strong structure, while their application on unstructured documents does not show significant improvement. Although this paper is not focused on segmenting unstructured documents, results obtained on a benchmark dataset are equal or even overcome previous results of similar works.  
  Address Tsukuba Science City, Japan  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ CrR2012 Serial 2051  
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Author Volkmar Frinken; Francisco Zamora; Salvador España; Maria Jose Castro; Andreas Fischer; Horst Bunke edit   pdf
isbn  openurl
  Title Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 701-704  
  Keywords  
  Abstract Unconstrained handwritten text recognition systems maximize the combination of two separate probability scores. The first one is the observation probability that indicates how well the returned word sequence matches the input image. The second score is the probability that reflects how likely a word sequence is according to a language model. Current state-of-the-art recognition systems use statistical language models in form of bigram word probabilities. This paper proposes to model the target language by means of a recurrent neural network with long-short term memory cells. Because the network is recurrent, the considered context is not limited to a fixed size especially as the memory cells are designed to deal with long-term dependencies. In a set of experiments conducted on the IAM off-line database we show the superiority of the proposed language model over statistical n-gram models.  
  Address Tsukuba Science City, Japan  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 1051-4651 ISBN 978-1-4673-2216-4 Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ FZE2012 Serial 2052  
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Author Marçal Rusiñol; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados edit   pdf
isbn  openurl
  Title Multipage Document Retrieval by Textual and Visual Representations Type Conference Article
  Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 521-524  
  Keywords  
  Abstract In this paper we present a multipage administrative document image retrieval system based on textual and visual representations of document pages. Individual pages are represented by textual or visual information using a bag-of-words framework. Different fusion strategies are evaluated which allow the system to perform multipage document retrieval on the basis of a single page retrieval system. Results are reported on a large dataset of document images sampled from a banking workflow.  
  Address Tsukuba Science City, Japan  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 1051-4651 ISBN 978-1-4673-2216-4 Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ RKB2012 Serial 2053  
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Author Marçal Rusiñol; Josep Llados edit  doi
isbn  openurl
  Title The Role of the Users in Handwritten Word Spotting Applications: Query Fusion and Relevance Feedback Type Conference Article
  Year 2012 Publication 13th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages 55-60  
  Keywords  
  Abstract In this paper we present the importance of including the user in the loop in a handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and a baseline word spotting approach based on a bag-of-visual-words model.  
  Address Bari, Italy  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-4673-2262-1 Medium  
  Area Expedition Conference ICFHR  
  Notes DAG Approved no  
  Call Number Admin @ si @ RuL2012 Serial 2054  
Permanent link to this record
 

 
Author Volkmar Frinken; Markus Baumgartner; Andreas Fischer; Horst Bunke edit   pdf
isbn  openurl
  Title Semi-Supervised Learning for Cursive Handwriting Recognition using Keyword Spotting Type Conference Article
  Year 2012 Publication 13th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages 49-54  
  Keywords  
  Abstract State-of-the-art handwriting recognition systems are learning-based systems that require large sets of training data. The creation of training data, and consequently the creation of a well-performing recognition system, requires therefore a substantial amount of human work. This can be reduced with semi-supervised learning, which uses unlabeled text lines for training as well. Current approaches estimate the correct transcription of the unlabeled data via handwriting recognition which is not only extremely demanding as far as computational costs are concerned but also requires a good model of the target language. In this paper, we propose a different approach that makes use of keyword spotting, which is significantly faster and does not need any language model. In a set of experiments we demonstrate its superiority over existing approaches.  
  Address Bari, Italy  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 10.1109/ICFHR.2012.268 ISBN 978-1-4673-2262-1 Medium  
  Area Expedition Conference ICFHR  
  Notes DAG Approved no  
  Call Number Admin @ si @ FBF2012 Serial 2055  
Permanent link to this record
 

 
Author Emanuel Indermühle; Volkmar Frinken; Horst Bunke edit   pdf
doi  isbn
openurl 
  Title Mode Detection in Online Handwritten Documents using BLSTM Neural Networks Type Conference Article
  Year 2012 Publication 13th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages 302-307  
  Keywords  
  Abstract Mode detection in online handwritten documents refers to the process of distinguishing different types of contents, such as text, formulas, diagrams, or tables, one from another. In this paper a new approach to mode detection is proposed that uses bidirectional long-short term memory (BLSTM) neural networks. The BLSTM neural network is a novel type of recursive neural network that has been successfully applied in speech and handwriting recognition. In this paper we show that it has the potential to significantly outperform traditional methods for mode detection, which are usually based on stroke classification. As a further advantage over previous approaches, the proposed system is trainable and does not rely on user-defined heuristics. Moreover, it can be easily adapted to new or additional types of modes by just providing the system with new training data.  
  Address Bari, italy  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-4673-2262-1 Medium  
  Area Expedition Conference ICFHR  
  Notes DAG Approved no  
  Call Number Admin @ si @ IFB2012 Serial 2056  
Permanent link to this record
 

 
Author Ernest Valveny; Robert Benavente; Agata Lapedriza; Miquel Ferrer; Jaume Garcia; Gemma Sanchez edit   pdf
doi  openurl
  Title Adaptation of a computer programming course to the EXHE requirements: evaluation five years later Type Miscellaneous
  Year 2012 Publication European Journal of Engineering Education Abbreviated Journal  
  Volume 37 Issue 3 Pages 243-254  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; CIC; OR; invisible;MV Approved no  
  Call Number Admin @ si @ VBL2012 Serial 2070  
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