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Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Horst Bunke |
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Title |
Writer Identification in Old Handwritten Music Scores |
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Conference Article |
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Year |
2008 |
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Proceedings of the 8th International Workshop on Document Analysis Systems, |
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347–353 |
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Nara (Japan) |
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DAG @ dag @ FLS2008b |
Serial |
1078 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Recognition of Multi-oriented Touching Characters in Graphical Documents |
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Conference Article |
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Year |
2008 |
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Computer Vision, Graphics & Image Processing, 2008. Sixth Indian Conference on, |
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16 |
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297–304 |
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ICVGIP ’08 |
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DAG @ dag @ RPL2008c |
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1080 |
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Author |
Muhammad Muzzamil Luqman; Thierry Brouard; Jean-Yves Ramel; Josep Llados |
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Title |
Recherche de sous-graphes par encapsulation floue des cliques d'ordre 2: Application à la localisation de contenu dans les images de documents graphiques |
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Conference Article |
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2012 |
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Colloque International Francophone sur l'Écrit et le Document |
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149-162 |
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CIFED |
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Call Number |
Admin @ si @ LBR2012 |
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2382 |
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Author |
Jose Antonio Rodriguez; Gemma Sanchez; Josep Llados |
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Title |
Categorization of Digital Ink Elements using Spectral Features |
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Book Chapter |
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Year |
2008 |
Publication |
Graphics Recognition: Recent Advances and New Opportunities |
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5046 |
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188–198 |
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Springer–Verlag |
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W. Liu, J. Llados, J.M. Ogier |
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DAG |
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DAG @ dag @ RSL2008 |
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1099 |
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Author |
Josep Llados; Dimosthenis Karatzas; Joan Mas; Gemma Sanchez |
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Title |
A Generic Architecture for the Conversion of Document Collections into Semantically Annotated Digital Archives |
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Journal |
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2008 |
Publication |
Journal of Universal Computer Science |
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14 |
Issue |
18 |
Pages |
2912–2935 |
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Keywords |
Median Graph, Graph Embedding, Graph Matching, Structural Pattern Recognition |
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DAG |
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DAG @ dag @ LKM2008 |
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1142 |
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Author |
Alicia Fornes; Josep Llados; Gemma Sanchez; Horst Bunke |
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Title |
Symbol-independent writer identification in old handwritten music scores |
Type |
Conference Article |
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Year |
2009 |
Publication |
In proceedings of 8th IAPR International Workshop on Graphics Recognition |
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Pages |
186–197 |
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Address |
La Rochelle, France |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-13727-3 |
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GREC |
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DAG |
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no |
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Call Number |
DAG @ dag @ FLS2009a |
Serial |
1222 |
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Author |
Agnes Borras; Josep Llados |
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Title |
Corest: A measure of color and space stability to detect salient regions according to human criteria |
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Conference Article |
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Year |
2009 |
Publication |
5th International Conference on Computer Vision Theory and Applications |
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204-209 |
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Lisboa, Portugal |
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978-989-8111-69-2 |
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VISAPP |
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DAG |
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DAG @ dag @ BoL2009 |
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1225 |
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Author |
Salim Jouili; Salvatore Tabbone; Ernest Valveny |
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Title |
Evaluation of graph matching measures for documents retrieval |
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Conference Article |
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Year |
2009 |
Publication |
In proceedings of 8th IAPR International Workshop on Graphics Recognition |
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13–21 |
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Graph Matching; Graph retrieval; structural representation; Performance Evaluation |
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In this paper we evaluate four graph distance measures. The analysis is performed for document retrieval tasks. For this aim, different kind of documents are used which include line drawings (symbols), ancient documents (ornamental letters), shapes and trademark-logos. The experimental results show that the performance of each grahp distance measure depends on the kind of data and the graph representation technique. |
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La Rochelle, France |
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0302-9743 |
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978-3-642-13727-3 |
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GREC |
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DAG |
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DAG @ dag @ JTV2009a |
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1230 |
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Author |
Partha Pratim Roy; Josep Llados; Umapada Pal |
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Title |
A Complete System for Detection and Recognition of Text in Graphical Documents using Background Information |
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Conference Article |
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Year |
2009 |
Publication |
5th International Conference on Computer Vision Theory and Applications |
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Lisboa, Portugal |
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978-989-8111-69-2 |
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VISAPP |
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DAG @ dag @ RLP2009 |
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1238 |
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Author |
Dena Bazazian |
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Title |
Fully Convolutional Networks for Text Understanding in Scene Images |
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2018 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Text understanding in scene images has gained plenty of attention in the computer vision community and it is an important task in many applications as text carries semantically rich information about scene content and context. For instance, reading text in a scene can be applied to autonomous driving, scene understanding or assisting visually impaired people. The general aim of scene text understanding is to localize and recognize text in scene images. Text regions are first localized in the original image by a trained detector model and afterwards fed into a recognition module. The tasks of localization and recognition are highly correlated since an inaccurate localization can affect the recognition task.
The main purpose of this thesis is to devise efficient methods for scene text understanding. We investigate how the latest results on deep learning can advance text understanding pipelines. Recently, Fully Convolutional Networks (FCNs) and derived methods have achieved a significant performance on semantic segmentation and pixel level classification tasks. Therefore, we took benefit of the strengths of FCN approaches in order to detect text in natural scenes. In this thesis we have focused on two challenging tasks of scene text understanding which are Text Detection and Word Spotting. For the task of text detection, we have proposed an efficient text proposal technique in scene images. We have considered the Text Proposals method as the baseline which is an approach to reduce the search space of possible text regions in an image. In order to improve the Text Proposals method we combined it with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same level of accuracy and thus gaining a significant speed up. Our experiments demonstrate that this text proposal approach yields significantly higher recall rates than the line based text localization techniques, while also producing better-quality localization. We have also applied this technique on compressed images such as videos from wearable egocentric cameras. For the task of word spotting, we have introduced a novel mid-level word representation method. We have proposed a technique to create and exploit an intermediate representation of images based on text attributes which roughly correspond to character probability maps. Our representation extends the concept of Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We call this representation the Soft-PHOC. Furthermore, we show how to use Soft-PHOC descriptors for word spotting tasks through an efficient text line proposal algorithm. To evaluate the detected text, we propose a novel line based evaluation along with the classic bounding box based approach. We test our method on incidental scene text images which comprises real-life scenarios such as urban scenes. The importance of incidental scene text images is due to the complexity of backgrounds, perspective, variety of script and language, short text and little linguistic context. All of these factors together makes the incidental scene text images challenging. |
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November 2018 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Dimosthenis Karatzas;Andrew Bagdanov |
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978-84-948531-1-1 |
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DAG; 600.121 |
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Admin @ si @ Baz2018 |
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3220 |
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