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Author Hana Jarraya; Oriol Ramos Terrades; Josep Llados edit  doi
openurl 
  Title Learning structural loss parameters on graph embedding applied on symbolic graphs Type Conference Article
  Year 2017 Publication 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset.  
  Address Kyoto; Japan; November 2017  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition (up)  
  ISSN ISBN Medium  
  Area Expedition Conference GREC  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ JRL2017b Serial 3073  
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Author Marçal Rusiñol; J. Chazalon; Katerine Diaz edit   pdf
doi  openurl
  Title Augmented Songbook: an Augmented Reality Educational Application for Raising Music Awareness Type Journal Article
  Year 2018 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 77 Issue 11 Pages 13773-13798  
  Keywords Augmented reality; Document image matching; Educational applications  
  Abstract This paper presents the development of an Augmented Reality mobile application which aims at sensibilizing young children to abstract concepts of music. Such concepts are, for instance, the musical notation or the idea of rhythm. Recent studies in Augmented Reality for education suggest that such technologies have multiple benefits for students, including younger ones. As mobile document image acquisition and processing gains maturity on mobile platforms, we explore how it is possible to build a markerless and real-time application to augment the physical documents with didactic animations and interactive virtual content. Given a standard image processing pipeline, we compare the performance of different local descriptors at two key stages of the process. Results suggest alternatives to the SIFT local descriptors, regarding result quality and computational efficiency, both for document model identification and perspective transform estimation. All experiments are performed on an original and public dataset we introduce here.  
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  Area Expedition Conference  
  Notes DAG; ADAS; 600.084; 600.121; 600.118; 600.129 Approved no  
  Call Number Admin @ si @ RCD2018 Serial 2996  
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Author J. Chazalon; P. Gomez-Kramer; Jean-Christophe Burie; M.Coustaty; S.Eskenazi; Muhammad Muzzamil Luqman; Nibal Nayef; Marçal Rusiñol; N. Sidere; Jean-Marc Ogier edit   pdf
doi  openurl
  Title SmartDoc 2017 Video Capture: Mobile Document Acquisition in Video Mode Type Conference Article
  Year 2017 Publication 1st International Workshop on Open Services and Tools for Document Analysis Abbreviated Journal  
  Volume Issue Pages  
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  Abstract As mobile document acquisition using smartphones is getting more and more common, along with the continuous improvement of mobile devices (both in terms of computing power and image quality), we can wonder to which extent mobile phones can replace desktop scanners. Modern applications can cope with perspective distortion and normalize the contrast of a document page captured with a smartphone, and in some cases like bottle labels or posters, smartphones even have the advantage of allowing the acquisition of non-flat or large documents. However, several cases remain hard to handle, such as reflective documents (identity cards, badges, glossy magazine cover, etc.) or large documents for which some regions require an important amount of detail. This paper introduces the SmartDoc 2017 benchmark (named “SmartDoc Video Capture”), which aims at
assessing whether capturing documents using the video mode of a smartphone could solve those issues. The task under evaluation is both a stitching and a reconstruction problem, as the user can move the device over different parts of the document to capture details or try to erase highlights. The material released consists of a dataset, an evaluation method and the associated tool, a sample method, and the tools required to extend the dataset. All the components are released publicly under very permissive licenses, and we particularly cared about maximizing the ease of
understanding, usage and improvement.
 
  Address Kyoto; Japan; November 2017  
  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 (up)  
  ISSN ISBN Medium  
  Area Expedition Conference ICDAR-OST  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ CGB2017 Serial 2997  
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Author Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas edit   pdf
doi  openurl
  Title LSDE: Levenshtein Space Deep Embedding for Query-by-string Word Spotting Type Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract n this paper we present the LSDE string representation and its application to handwritten word spotting. LSDE is a novel embedding approach for representing strings that learns a space in which distances between projected points are correlated with the Levenshtein edit distance between the original strings.
We show how such a representation produces a more semantically interpretable retrieval from the user’s perspective than other state of the art ones such as PHOC and DCToW. We also conduct a preliminary handwritten word spotting experiment on the George Washington dataset.
 
  Address Kyoto; Japan; November 2017  
  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 (up)  
  ISSN ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ GRK2017 Serial 2999  
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Author E. Royer; J. Chazalon; Marçal Rusiñol; F. Bouchara edit   pdf
doi  openurl
  Title Benchmarking Keypoint Filtering Approaches for Document Image Matching Type Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Best Poster Award.
Reducing the amount of keypoints used to index an image is particularly interesting to control processing time and memory usage in real-time document image matching applications, like augmented documents or smartphone applications. This paper benchmarks two keypoint selection methods on a task consisting of reducing keypoint sets extracted from document images, while preserving detection and segmentation accuracy. We first study the different forms of keypoint filtering, and we introduce the use of the CORE selection method on
keypoints extracted from document images. Then, we extend a previously published benchmark by including evaluations of the new method, by adding the SURF-BRISK detection/description scheme, and by reporting processing speeds. Evaluations are conducted on the publicly available dataset of ICDAR2015 SmartDOC challenge 1. Finally, we prove that reducing the original keypoint set is always feasible and can be beneficial
not only to processing speed but also to accuracy.
 
  Address Kyoto; Japan; November 2017  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition (up)  
  ISSN ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ RCR2017 Serial 3000  
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Author David Aldavert; Marçal Rusiñol; Ricardo Toledo edit   pdf
doi  openurl
  Title Automatic Static/Variable Content Separation in Administrative Document Images Type Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract In this paper we present an automatic method for separating static and variable content from administrative document images. An alignment approach is able to unsupervisedly build probabilistic templates from a set of examples of the same document kind. Such templates define which is the likelihood of every pixel of being either static or variable content. In the extraction step, the same alignment technique is used to match
an incoming image with the template and to locate the positions where variable fields appear. We validate our approach on the public NIST Structured Tax Forms Dataset.
 
  Address Kyoto; Japan; November 2017  
  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 (up)  
  ISSN ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.084; 600.121;ADAS Approved no  
  Call Number Admin @ si @ ART2017 Serial 3001  
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Author Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas edit   pdf
url  openurl
  Title Learning to Learn from Web Data through Deep Semantic Embeddings Type Conference Article
  Year 2018 Publication 15th European Conference on Computer Vision Workshops Abbreviated Journal  
  Volume 11134 Issue Pages 514-529  
  Keywords  
  Abstract In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the pipeline can learn from images with associated text without supervision and perform a thourough analysis of five different text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art in the MIRFlickr dataset when training in the target data. Further we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings.  
  Address Munich; Alemanya; September 2018  
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  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition (up)  
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  Area Expedition Conference ECCVW  
  Notes DAG; 600.129; 601.338; 600.121 Approved no  
  Call Number Admin @ si @ GGG2018a Serial 3175  
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Author Arka Ujjal Dey; Suman Ghosh; Ernest Valveny edit   pdf
openurl 
  Title Don't only Feel Read: Using Scene text to understand advertisements Type Conference Article
  Year 2018 Publication IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal  
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  Abstract We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, and can thus aid in classifcation tasks. To this end, we develop a framework using off-the-shelf components, and demonstrate the effectiveness of Textual cues in semantic Classfication tasks.  
  Address Salt Lake City; Utah; USA; June 2018  
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  Area Expedition Conference CVPRW  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ DGV2018 Serial 3551  
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Author Leonardo Galteri; Dena Bazazian; Lorenzo Seidenari; Marco Bertini; Andrew Bagdanov; Anguelos Nicolaou; Dimosthenis Karatzas; Alberto del Bimbo edit   pdf
doi  openurl
  Title Reading Text in the Wild from Compressed Images Type Conference Article
  Year 2017 Publication 1st International workshop on Egocentric Perception, Interaction and Computing Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Reading text in the wild is gaining attention in the computer vision community. Images captured in the wild are almost always compressed to varying degrees, depending on application context, and this compression introduces artifacts
that distort image content into the captured images. In this paper we investigate the impact these compression artifacts have on text localization and recognition in the wild. We also propose a deep Convolutional Neural Network (CNN) that can eliminate text-specific compression artifacts and which leads to an improvement in text recognition. Experimental results on the ICDAR-Challenge4 dataset demonstrate that compression artifacts have a significant
impact on text localization and recognition and that our approach yields an improvement in both – especially at high compression rates.
 
  Address Venice; Italy; October 2017  
  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition (up)  
  ISSN ISBN Medium  
  Area Expedition Conference ICCV - EPIC  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ GBS2017 Serial 3006  
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Author N. Nayef; F. Yin; I. Bizid; H .Choi; Y. Feng; Dimosthenis Karatzas; Z. Luo; Umapada Pal; Christophe Rigaud; J. Chazalon; W. Khlif; Muhammad Muzzamil Luqman; Jean-Christophe Burie; C.L. Liu; Jean-Marc Ogier edit  doi
isbn  openurl
  Title ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification – RRC-MLT Type Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 1454-1459  
  Keywords  
  Abstract Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge.  
  Address Kyoto; Japan; November 2017  
  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition (up)  
  ISSN ISBN 978-1-5386-3586-5 Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ NYB2017 Serial 3097  
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