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Author Josep Llados; Horst Bunke; Enric Marti edit   pdf
url  doi
openurl 
  Title (down) Finding rotational symmetries by cyclic string matching Type Journal Article
  Year 1997 Publication Pattern recognition letters Abbreviated Journal PRL  
  Volume 18 Issue 14 Pages 1435-1442  
  Keywords Rotational symmetry; Reflectional symmetry; String matching  
  Abstract Symmetry is an important shape feature. In this paper, a simple and fast method to detect perfect and distorted rotational symmetries of 2D objects is described. The boundary of a shape is polygonally approximated and represented as a string. Rotational symmetries are found by cyclic string matching between two identical copies of the shape string. The set of minimum cost edit sequences that transform the shape string to a cyclically shifted version of itself define the rotational symmetry and its order. Finally, a modification of the algorithm is proposed to detect reflectional symmetries. Some experimental results are presented to show the reliability of the proposed algorithm  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG;IAM; Approved no  
  Call Number IAM @ iam @ LBM1997a Serial 1562  
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Author Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier edit   pdf
openurl 
  Title (down) Filtrage de descripteurs locaux pour l'amélioration de la détection de documents Type Conference Article
  Year 2016 Publication Colloque International Francophone sur l'Écrit et le Document Abbreviated Journal  
  Volume Issue Pages  
  Keywords Local descriptors; mobile capture; document matching; keypoint selection  
  Abstract In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework.In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements.  
  Address Toulouse; France; March 2016  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CIFED  
  Notes DAG; 600.084; 600.077 Approved no  
  Call Number Admin @ si @ RCO2016 Serial 2755  
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Author Marçal Rusiñol; T.Benkhelfallah; V. Poulain d'Andecy edit   pdf
doi  openurl
  Title (down) Field Extraction from Administrative Documents by Incremental Structural Templates Type Conference Article
  Year 2013 Publication 12th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 1100 - 1104  
  Keywords  
  Abstract In this paper we present an incremental framework aimed at extracting field information from administrative document images in the context of a Digital Mail-room scenario. Given a single training sample in which the user has marked which fields have to be extracted from a particular document class, a document model representing structural relationships among words is built. This model is incrementally refined as the system processes more and more documents from the same class. A reformulation of the tf-idf statistic scheme allows to adjust the importance weights of the structural relationships among words. We report in the experimental section our results obtained with a large dataset of real invoices.  
  Address Washington; USA; August 2013  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1520-5363 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.56; 600.045; 605.203; 602.101 Approved no  
  Call Number Admin @ si @ RBP2013 Serial 2346  
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Author V. Poulain d'Andecy; Emmanuel Hartmann; Marçal Rusiñol edit   pdf
doi  openurl
  Title (down) Field Extraction by hybrid incremental and a-priori structural templates Type Conference Article
  Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 251 - 256  
  Keywords Layout Analysis; information extraction; incremental learning  
  Abstract In this paper, we present an incremental framework for extracting information fields from administrative documents. First, we demonstrate some limits of the existing state-of-the-art methods such as the delay of the system efficiency. This is a concern in industrial context when we have only few samples of each document class. Based on this analysis, we propose a hybrid system combining incremental learning by means of itf-df statistics and a-priori generic
models. We report in the experimental section our results obtained with a dataset of real invoices.
 
  Address Viena; Austria; April 2018  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference DAS  
  Notes DAG; 600.084; 600.129; 600.121 Approved no  
  Call Number Admin @ si @ PHR2018 Serial 3106  
Permanent link to this record
 

 
Author Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi edit  doi
openurl 
  Title (down) Few shots are all you need: A progressive learning approach for low resource handwritten text recognition Type Journal Article
  Year 2022 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 160 Issue Pages 43-49  
  Keywords  
  Abstract Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.121; 600.162; 602.230 Approved no  
  Call Number Admin @ si @ SFK2022 Serial 3736  
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Author Jaume Gibert; Ernest Valveny; Horst Bunke edit   pdf
doi  openurl
  Title (down) Feature Selection on Node Statistics Based Embedding of Graphs Type Journal Article
  Year 2012 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 33 Issue 15 Pages 1980–1990  
  Keywords Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification  
  Abstract 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.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ GVB2012b Serial 1993  
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Author H. Chouaib; Oriol Ramos Terrades; Salvatore Tabbone; F. Cloppet; N. Vincent edit  doi
openurl 
  Title (down) Feature Selection Combining Genetic Algorithm and Adaboost Classifiers Type Conference Article
  Year 2008 Publication 19th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 1-4  
  Keywords  
  Abstract  
  Address Tampa, Florida  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG Approved no  
  Call Number Admin @ si @ CRT2008 Serial 1872  
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Author Katerine Diaz; Jesus Martinez del Rincon; Marçal Rusiñol; Aura Hernandez-Sabate edit   pdf
doi  openurl
  Title (down) Feature Extraction by Using Dual-Generalized Discriminative Common Vectors Type Journal Article
  Year 2019 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV  
  Volume 61 Issue 3 Pages 331-351  
  Keywords Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning  
  Abstract In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; ADAS; 600.084; 600.118; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ DRR2019 Serial 3172  
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Author Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov edit   pdf
url  openurl
  Title (down) Fast: Facilitated and accurate scene text proposals through fcn guided pruning Type Journal Article
  Year 2019 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 119 Issue Pages 112-120  
  Keywords  
  Abstract Class-specific text proposal algorithms can efficiently reduce the search space for possible text object locations in an image. In this paper we combine the Text Proposals algorithm with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same recall level and thus gaining a significant speed up. Our experiments demonstrate that such text proposal approaches yield significantly higher recall rates than state-of-the-art text localization techniques, while also producing better-quality localizations. Our results on the ICDAR 2015 Robust Reading Competition (Challenge 4) and the COCO-text datasets show that, when combined with strong word classifiers, this recall margin leads to state-of-the-art results in end-to-end scene text recognition.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.084; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ BGN2019 Serial 3342  
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Author Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados edit   pdf
doi  openurl
  Title (down) Fast Structural Matching for Document Image Retrieval through Spatial Databases Type Conference Article
  Year 2014 Publication Document Recognition and Retrieval XXI Abbreviated Journal  
  Volume 9021 Issue Pages  
  Keywords Document image retrieval; distance transform; MSER; spatial database  
  Abstract The structure of document images plays a signi cant role in document analysis thus considerable e orts have been made towards extracting and understanding document structure, usually in the form of layout analysis approaches. In this paper, we rst employ Distance Transform based MSER (DTMSER) to eciently extract stable document structural elements in terms of a dendrogram of key-regions. Then a fast structural matching method is proposed to query the structure of document (dendrogram) based on a spatial database which facilitates the formulation of advanced spatial queries. The experiments demonstrate a signi cant improvement in a document retrieval scenario when compared to the use of typical Bag of Words (BoW) and pyramidal BoW descriptors.  
  Address Amsterdam; September 2014  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference SPIE-DRR  
  Notes DAG; 600.056; 600.061; 600.077 Approved no  
  Call Number Admin @ si @ GRK2014a Serial 2496  
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