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Oriol Ramos Terrades and Ernest Valveny. 2003. Line Detection Using Ridgelets Transform for Graphic Symbol Representation.
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Oriol Ramos Terrades and Ernest Valveny. 2003. Indexing Technical Symbols Using Ridgelets Transform.
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Oriol Ramos Terrades and Ernest Valveny. 2003. Radon Transform for Lineal Symbol Representation.
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Oriol Ramos Terrades and Ernest Valveny. 2004. Indexing Technical Symbols Using Ridgelets Transform.
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Oriol Ramos Terrades and Ernest Valveny. 2006. A new use of the ridgelets transform for describing linear singularities in images. PRL, 27(6), 587–596.
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Oriol Ramos Terrades and Ernest Valveny. 2005. Local Norm Features based on ridgelets Transform.
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Oriol Ramos Terrades, Ernest Valveny and Salvatore Tabbone. 2007. On the Combination of Ridgelets Descriptors for Symbol Recognition. Seventh IAPR International Workshop on Graphics Recognition.18–20.
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Oriol Ramos Terrades, Ernest Valveny and Salvatore Tabbone. 2008. On the Combination of Ridgelets Descriptors for Symbol Recognition. Graphics Recognition: Recent Advances and New Oportunities, W. Lius, J. Llados, J.M. Ogier, LNCS 5046:104–113.
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Oriol Ramos Terrades, Ernest Valveny and Salvatore Tabbone. 2009. Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework. TPAMI, 31(9), 1630–1644.
Abstract: 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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Oriol Ramos Terrades, N. Serrano, Albert Gordo, Ernest Valveny and Alfons Juan-Ciscar. 2010. Interactive-predictive detection of handwritten text blocks. 17th Document Recognition and Retrieval Conference, part of the IS&T-SPIE Electronic Imaging Symposium.75340Q–75340Q–10.
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.
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