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Author  |
Oriol Vicente; Alicia Fornes; Ramon Valdes |

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Title |
The Digital Humanities Network of the UABCie: a smart structure of research and social transference for the digital humanities |
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2016 |
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Digital Humanities Centres: Experiences and Perspectives |
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Warsaw; Poland; December 2016 |
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DAG; 600.097 |
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Admin @ si @ VFV2016 |
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2908 |
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Author  |
Oriol Vicente; Alicia Fornes; Ramon Valdes |


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Title |
La Xarxa d Humanitats Digitals de la UABCie: una estructura inteligente para la investigación y la transferencia en Humanidades |
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Conference Article |
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2017 |
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3rd Congreso Internacional de Humanidades Digitales Hispánicas. Sociedad Internacional |
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281-383 |
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978-84-697-5692-8 |
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HDH |
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DAG; 600.121 |
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Admin @ si @ VFV2017 |
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3060 |
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Author  |
Oriol Ramos Terrades; Salvatore Tabbone; L. Wendling; Ernest Valveny |

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Title |
Symbol Recognition based on a Multiresolution Analysis of the Radon Transform |
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Miscellaneous |
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2004 |
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The International Workshop on Multidisciplinary Image, Video, and Audio Retrieval and Mining |
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Sherbrooke (Canada) |
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DAG |
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DAG @ dag @ RTW2004 |
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500 |
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Oriol Ramos Terrades; Salvatore Tabbone; Ernest Valveny |

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Title |
Combination of shape descriptors using an adaptation of boosting |
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Miscellaneous |
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2006 |
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18th International Conference on Pattern Recognition (ICPR´06), 2: 764–767 |
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Hong Kong |
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DAG |
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DAG @ dag @ RTV2006 |
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718 |
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Author  |
Oriol Ramos Terrades; Salvatore Tabbone; Ernest Valveny |

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Title |
A Review of Shape Descriptors for Document Analysis |
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Conference Article |
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2007 |
Publication |
9th International Conference on Document Analysis and Recognition |
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1 |
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227–231 |
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Curitiba (Brazil) |
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DAG |
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DAG @ dag @ RTV2007b |
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884 |
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Author  |
Oriol Ramos Terrades; Salvatore Tabbone; Ernest Valveny |

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Title |
Optimal Linear Combination for Two-class Classifiers |
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Conference Article |
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2007 |
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Proceedings of the International Conference on Advances in Pattern Recognition |
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Kolkata (India) |
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ICAPR |
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DAG @ dag @ RTV2007a |
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894 |
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Author  |
Oriol Ramos Terrades; N. Serrano; Albert Gordo; Ernest Valveny; Alfons Juan-Ciscar |

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Title |
Interactive-predictive detection of handwritten text blocks |
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Conference Article |
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2010 |
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17th Document Recognition and Retrieval Conference, part of the IS&T-SPIE Electronic Imaging Symposium |
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7534 |
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75340Q–75340Q–10 |
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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. |
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DAG @ dag @ TSG2010 |
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1479 |
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Author  |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |

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Title |
On the Combination of Ridgelets Descriptors for Symbol Recognition |
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Conference Article |
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2007 |
Publication |
Seventh IAPR International Workshop on Graphics Recognition |
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18–20 |
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Curitiba (Brazil) |
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GREC |
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DAG |
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DAG @ dag @ RVT2007 |
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886 |
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Author  |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |

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Title |
On the Combination of Ridgelets Descriptors for Symbol Recognition |
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Book Chapter |
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2008 |
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Graphics Recognition: Recent Advances and New Oportunities, W. Lius, J. Llados, J.M. Ogier, LNCS 5046:104–113 |
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DAG |
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DAG @ dag @ RVT2008 |
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984 |
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Author  |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |

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Title |
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework |
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Journal Article |
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2009 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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31 |
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9 |
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1630–1644 |
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The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes. |
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0162-8828 |
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DAG @ dag @ RVT2009 |
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1220 |
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