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Veronica Romero; Alicia Fornes; Nicolas Serrano; Joan Andreu Sanchez; A.H. Toselli; Volkmar Frinken; E. Vidal; Josep Llados |
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Title |
The ESPOSALLES database: An ancient marriage license corpus for off-line handwriting recognition |
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Journal Article |
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2013 |
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Pattern Recognition |
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PR |
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46 |
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6 |
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1658-1669 |
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Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demography studies and genealogical research. Automatic processing of historical documents, however, has mostly been focused on single works of literature and less on social records, which tend to have a distinct layout, structure, and vocabulary. Such information is usually collected by expert demographers that devote a lot of time to manually transcribe them. This paper presents a new database, compiled from a marriage license books collection, to support research in automatic handwriting recognition for historical documents containing social records. Marriage license books are documents that were used for centuries by ecclesiastical institutions to register marriage licenses. Books from this collection are handwritten and span nearly half a millennium until the beginning of the 20th century. In addition, a study is presented about the capability of state-of-the-art handwritten text recognition systems, when applied to the presented database. Baseline results are reported for reference in future studies. |
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Elsevier Science Inc. New York, NY, USA |
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0031-3203 |
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DAG; 600.045; 602.006; 605.203 |
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Admin @ si @ RFS2013 |
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2298 |
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Alicia Fornes; Anjan Dutta; Albert Gordo; Josep Llados |
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Title |
CVC-MUSCIMA: A Ground-Truth of Handwritten Music Score Images for Writer Identification and Staff Removal |
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Journal Article |
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2012 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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15 |
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3 |
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243-251 |
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Music scores; Handwritten documents; Writer identification; Staff removal; Performance evaluation; Graphics recognition; Ground truths |
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0,405JCR
The analysis of music scores has been an active research field in the last decades. However, there are no publicly available databases of handwritten music scores for the research community. In this paper we present the CVC-MUSCIMA database and ground-truth of handwritten music score images. The dataset consists of 1,000 music sheets written by 50 different musicians. It has been especially designed for writer identification and staff removal tasks. In addition to the description of the dataset, ground-truth, partitioning and evaluation metrics, we also provide some base-line results for easing the comparison between different approaches. |
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1433-2833 |
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Admin @ si @ FDG2012 |
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2129 |
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Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Feature Selection on Node Statistics Based Embedding of Graphs |
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Journal Article |
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2012 |
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Pattern Recognition Letters |
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PRL |
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33 |
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15 |
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1980–1990 |
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Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification |
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Representing a graph with a feature vector is a common way of making statistical machine learning algorithms applicable to the domain of graphs. Such a transition from graphs to vectors is known as graphembedding. A key issue in graphembedding is to select a proper set of features in order to make the vectorial representation of graphs as strong and discriminative as possible. In this article, we propose features that are constructed out of frequencies of node label representatives. We first build a large set of features and then select the most discriminative ones according to different ranking criteria and feature transformation algorithms. On different classification tasks, we experimentally show that only a small significant subset of these features is needed to achieve the same classification rates as competing to state-of-the-art methods. |
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Admin @ si @ GVB2012b |
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1993 |
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Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Graph Embedding in Vector Spaces by Node Attribute Statistics |
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2012 |
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Pattern Recognition |
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PR |
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45 |
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9 |
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3072-3083 |
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Structural pattern recognition; Graph embedding; Data clustering; Graph classification |
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Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graphembedding into vectorspaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous nodeattributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs. |
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0031-3203 |
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Admin @ si @ GVB2012a |
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1992 |
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Author |
Jon Almazan; Alicia Fornes; Ernest Valveny |
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Title |
A non-rigid appearance model for shape description and recognition |
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Journal Article |
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2012 |
Publication |
Pattern Recognition |
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PR |
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45 |
Issue |
9 |
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3105--3113 |
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Shape recognition; Deformable models; Shape modeling; Hand-drawn recognition |
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In this paper we describe a framework to learn a model of shape variability in a set of patterns. The framework is based on the Active Appearance Model (AAM) and permits to combine shape deformations with appearance variability. We have used two modifications of the Blurred Shape Model (BSM) descriptor as basic shape and appearance features to learn the model. These modifications permit to overcome the rigidity of the original BSM, adapting it to the deformations of the shape to be represented. We have applied this framework to representation and classification of handwritten digits and symbols. We show that results of the proposed methodology outperform the original BSM approach. |
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0031-3203 |
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DAG @ dag @ AFV2012 |
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1982 |
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