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Author Pau Riba; Anjan Dutta; Josep Llados; Alicia Fornes edit   pdf
openurl 
  Title Graph-based deep learning for graphics classification Type (up) Conference Article
  Year 2017 Publication 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages 29-30  
  Keywords  
  Abstract Graph-based representations are a common way to deal with graphics recognition problems. However, previous works were mainly focused on developing learning-free techniques. The success of deep learning frameworks have proved that learning is a powerful tool to solve many problems, however it is not straightforward to extend these methodologies to non euclidean data such as graphs. On the other hand, graphs are a good representational structure for graphical entities. In this work, we present some deep learning techniques that have been proposed in the literature for graph-based representations and
we show how they can be used in graphics recognition problems
 
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  Area Expedition Conference GREC  
  Notes DAG; 600.097; 601.302; 600.121 Approved no  
  Call Number Admin @ si @ RDL2017b Serial 3058  
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Author Adria Rico; Alicia Fornes edit   pdf
openurl 
  Title Camera-based Optical Music Recognition using a Convolutional Neural Network Type (up) Conference Article
  Year 2017 Publication 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages 27-28  
  Keywords optical music recognition; document analysis; convolutional neural network; deep learning  
  Abstract Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results  
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  Area Expedition Conference GREC  
  Notes DAG;600.097; 600.121 Approved no  
  Call Number Admin @ si @ RiF2017 Serial 3059  
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Author Oriol Vicente; Alicia Fornes; Ramon Valdes edit   pdf
isbn  openurl
  Title La Xarxa d Humanitats Digitals de la UABCie: una estructura inteligente para la investigación y la transferencia en Humanidades Type (up) Conference Article
  Year 2017 Publication 3rd Congreso Internacional de Humanidades Digitales Hispánicas. Sociedad Internacional Abbreviated Journal  
  Volume Issue Pages 281-383  
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  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-697-5692-8 Medium  
  Area Expedition Conference HDH  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ VFV2017 Serial 3060  
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Author Alicia Fornes; Beata Megyesi; Joan Mas edit   pdf
openurl 
  Title Transcription of Encoded Manuscripts with Image Processing Techniques Type (up) Conference Article
  Year 2017 Publication Digital Humanities Conference Abbreviated Journal  
  Volume Issue Pages 441-443  
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  ISSN ISBN Medium  
  Area Expedition Conference DH  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ FMM2017 Serial 3061  
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Author Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol edit   pdf
openurl 
  Title The Robust Reading Competition Annotation and Evaluation Platform Type (up) 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 The ICDAR Robust Reading Competition (RRC), initiated in 2003 and re-established in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation
of data, and to provide online and offline performance evaluation and analysis services
 
  Address Kyoto; Japan; November 2017  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference ICDAR-OST  
  Notes DAG; 600.084; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ KGR2017 Serial 3063  
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Author Raul Gomez; Baoguang Shi; Lluis Gomez; Lukas Numann; Andreas Veit; Jiri Matas; Serge Belongie; Dimosthenis Karatzas edit  openurl
  Title ICDAR2017 Robust Reading Challenge on COCO-Text Type (up) Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address Kyoto; Japan; November 2017  
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  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ GSG2017 Serial 3076  
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Author Masakazu Iwamura; Naoyuki Morimoto; Keishi Tainaka; Dena Bazazian; Lluis Gomez; Dimosthenis Karatzas edit  doi
openurl 
  Title ICDAR2017 Robust Reading Challenge on Omnidirectional Video Type (up) Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Results of ICDAR 2017 Robust Reading Challenge on Omnidirectional Video are presented. This competition uses Downtown Osaka Scene Text (DOST) Dataset that was captured in Osaka, Japan with an omnidirectional camera. Hence, it consists of sequential images (videos) of different view angles. Regarding the sequential images as videos (video mode), two tasks of localisation and end-to-end recognition are prepared. Regarding them as a set of still images (still image mode), three tasks of localisation, cropped word recognition and end-to-end recognition are prepared. As the dataset has been captured in Japan, the dataset contains Japanese text but also include text consisting of alphanumeric characters (Latin text). Hence, a submitted result for each task is evaluated in three ways: using Japanese only ground truth (GT), using Latin only GT and using combined GTs of both. Finally, by the submission deadline, we have received two submissions in the text localisation task of the still image mode. We intend to continue the competition in the open mode. Expecting further submissions, in this report we provide baseline results in all the tasks in addition to the submissions from the community.  
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ IMT2017 Serial 3077  
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Author Suman Ghosh; Ernest Valveny edit   pdf
doi  openurl
  Title R-PHOC: Segmentation-Free Word Spotting using CNN Type (up) Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords Convolutional neural network; Image segmentation; Artificial neural network; Nearest neighbor search  
  Abstract arXiv:1707.01294
This paper proposes a region based convolutional neural network for segmentation-free word spotting. Our network takes as input an image and a set of word candidate bound- ing boxes and embeds all bounding boxes into an embedding space, where word spotting can be casted as a simple nearest neighbour search between the query representation and each of the candidate bounding boxes. We make use of PHOC embedding as it has previously achieved significant success in segmentation- based word spotting. Word candidates are generated using a simple procedure based on grouping connected components using some spatial constraints. Experiments show that R-PHOC which operates on images directly can improve the current state-of- the-art in the standard GW dataset and performs as good as PHOCNET in some cases designed for segmentation based word spotting.
 
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ GhV2017a Serial 3079  
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Author Suman Ghosh; Ernest Valveny edit   pdf
doi  openurl
  Title Visual attention models for scene text recognition Type (up) Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract arXiv:1706.01487
In this paper we propose an approach to lexicon-free recognition of text in scene images. Our approach relies on a LSTM-based soft visual attention model learned from convolutional features. A set of feature vectors are derived from an intermediate convolutional layer corresponding to different areas of the image. This permits encoding of spatial information into the image representation. In this way, the framework is able to learn how to selectively focus on different parts of the image. At every time step the recognizer emits one character using a weighted combination of the convolutional feature vectors according to the learned attention model. Training can be done end-to-end using only word level annotations. In addition, we show that modifying the beam search algorithm by integrating an explicit language model leads to significantly better recognition results. We validate the performance of our approach on standard SVT and ICDAR'03 scene text datasets, showing state-of-the-art performance in unconstrained text recognition.
 
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  ISSN ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ GhV2017b Serial 3080  
Permanent link to this record
 

 
Author Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov edit   pdf
openurl 
  Title Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images Type (up) Conference Article
  Year 2018 Publication International Workshop on Egocentric Perception, Interaction and Computing at ECCV Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Word spotting in natural scene images has many applications in scene understanding and visual assistance. We propose Soft-PHOC, an intermediate representation of images based on character probability maps. Our representation extends the concept of the 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 show how to use our descriptors for word spotting tasks in egocentric camera streams through an efficient text line proposal algorithm. This is based on the Hough Transform over character attribute maps followed by scoring using Dynamic Time Warping (DTW). We evaluate our results on ICDAR 2015 Challenge 4 dataset of incidental scene text captured by an egocentric camera.  
  Address Munich; Alemanya; September 2018  
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  Area Expedition Conference ECCVW  
  Notes DAG; 600.129; 600.121; Approved no  
  Call Number Admin @ si @ BKB2018b Serial 3174  
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