Marçal Rusiñol. (2009). Geometric and Structural-based Symbol Spotting. Application to Focused Retrieval in Graphic Document Collections (Josep Llados, Ed.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Usually, pattern recognition systems consist of two main parts. On the one hand, the data acquisition and, on the other hand, the classification of this data on a certain category. In order to recognize which category a certain query element belongs to, a set of pattern models must be provided beforehand. An off-line learning stage is needed to train the classifier and to offer a robust classification of the patterns. Within the pattern recognition field, we are interested in the recognition of graphics and, in particular, on the analysis of documents rich in graphical information. In this context, one of the main concerns is to see if the proposed systems remain scalable with respect to the data volume so as it can handle growing amounts of symbol models. In order to avoid to work with a database of reference symbols, symbol spotting and on-the-fly symbol recognition methods have been introduced in the past years.
Generally speaking, the symbol spotting problem can be defined as the identification of a set of regions of interest from a document image which are likely to contain an instance of a certain queriedn symbol without explicitly applying the whole pattern recognition scheme. Our application framework consists on indexing a collection of graphic-rich document images. This collection is
queried by example with a single instance of the symbol to look for and, by means of symbol spotting methods we retrieve the regions of interest where the symbol is likely to appear within the documents. This kind of applications are known as focused retrieval methods.
In order that the focused retrieval application can handle large collections of documents there is a need to provide an efficient access to the large volume of information that might be stored. We use indexing strategies in order to efficiently retrieve by similarity the locations where a certain part of the symbol appears. In that scenario, graphical patterns should be used as indices for accessing and navigating the collection of documents.
These indexing mechanism allow the user to search for similar elements using graphical information rather than textual queries.
Along this thesis we present a spotting architecture and different methods aiming to build a complete focused retrieval application dealing with a graphic-rich document collections. In addition, a protocol to evaluate the performance of symbol
spotting systems in terms of recognition abilities, location accuracy and scalability is proposed.
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Oriol Ramos Terrades, Ernest Valveny, & Salvatore Tabbone. (2007). On the Combination of Ridgelets Descriptors for Symbol Recognition. In Seventh IAPR International Workshop on Graphics Recognition (18–20).
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Oriol Ramos Terrades, Ernest Valveny, & Salvatore Tabbone. (2008). On the Combination of Ridgelets Descriptors for Symbol Recognition. In 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, & Salvatore Tabbone. (2009). Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 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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Jaume Rodriguez, S. Yacoub, Gemma Sanchez, & Josep Llados. (2006). Performance Evaluation, Comparison and Combination of Commercial Handwriting Recognition Engines.
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Gemma Sanchez, & Josep Llados. (2003). Syntactic models to represent perceptually regular repetitive patterns in graphic documents.
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Gemma Sanchez, & Josep Llados. (2004). Syntactic models to represent perceptually regular repetitive patterns in graphic documents.
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Gemma Sanchez, Alicia Fornes, Joan Mas, & Josep Llados. (2007). Computer Vision Tools for Visually Impaired Children Learning.
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Gemma Sanchez, Alicia Fornes, Joan Mas, & Josep Llados. (2007). Computer Vision Tools for Visually Impaired Children Learning.
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Gemma Sanchez, & Josep Llados. (2001). A Graph Grammar to Recognize Textured Symbols..
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Gemma Sanchez, Josep Llados, & K. Tombre. (2000). A mean string algorithm to compute the average among a set of 2D shapes.
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Gemma Sanchez, Josep Llados, & K. Tombre. (2001). An Algorithm to Recognize Graphical Textured Symbols using String Representations..
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Gemma Sanchez, Josep Llados, & K. Tombre. (2001). An Error-Correction Graph Grammar to Recognize Textured Symbols..
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Gemma Sanchez, Josep Llados, & K. Tombre. (2002). A mean string algorithm to compute the average among a set of 2D shapes. PRL - Pattern Recognition Letters, 23(1-3), 203–214.
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Gemma Sanchez, Ernest Valveny, Josep Llados, Joan Mas, & N. Lozano. (2004). A platform to extract knowledge from graphic documents. Application to an architectural sketch understanding scenario.
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