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Author Lasse Martensson; Anders Hast; Alicia Fornes edit   pdf
isbn  openurl
  Title Word Spotting as a Tool for Scribal Attribution Type Conference Article
  Year (down) 2017 Publication 2nd Conference of the association of Digital Humanities in the Nordic Countries Abbreviated Journal  
  Volume Issue Pages 87-89  
  Keywords  
  Abstract  
  Address Gothenburg; Suecia; March 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  
  ISSN ISBN 978-91-88348-83-8 Medium  
  Area Expedition Conference DHN  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ MHF2017 Serial 2954  
Permanent link to this record
 

 
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 (down) 2017 Publication 1st International workshop on Egocentric Perception, Interaction and Computing Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  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  
  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 ICCV - EPIC  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ GBS2017 Serial 3006  
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Author Lluis Gomez; Dimosthenis Karatzas edit   pdf
url  openurl
  Title TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild Type Journal Article
  Year (down) 2017 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 70 Issue Pages 60-74  
  Keywords  
  Abstract Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.

Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals.
 
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  Area Expedition Conference  
  Notes DAG; 600.084; 601.197; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ GoK2017 Serial 2886  
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Author Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas edit   pdf
doi  openurl
  Title Improving patch‐based scene text script identification with ensembles of conjoined networks Type Journal Article
  Year (down) 2017 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 67 Issue Pages 85-96  
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  Address  
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  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 @ GNK2017 Serial 2887  
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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 (down) 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  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  
  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 Lluis Gomez; Y. Patel; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Self‐supervised learning of visual features through embedding images into text topic spaces Type Conference Article
  Year (down) 2017 Publication 30th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible. In this paper we present a method that is able to take advantage of freely available multi-modal content to train computer vision algorithms without human supervision. We put forward the idea of performing self-supervised learning of visual features by mining a large scale corpus of multi-modal (text and image) documents. We show that discriminative visual features can be learnt efficiently by training a CNN to predict the semantic context in which a particular image is more probable to appear as an illustration. For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique. Our experiments demonstrate state of the art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or natural-supervised approaches.  
  Address Honolulu; Hawaii; July 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  
  ISSN ISBN Medium  
  Area Expedition Conference CVPR  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ GPR2017 Serial 2889  
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Author Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados edit  url
openurl 
  Title Ontology-Based Understanding of Architectural Drawings Type Book Chapter
  Year (down) 2017 Publication International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges Abbreviated Journal  
  Volume 9657 Issue Pages 75-85  
  Keywords Graphics recognition; Floor plan analysi; Domain ontology  
  Abstract In this paper we present a knowledge base of architectural documents aiming at improving existing methods of floor plan classification and understanding. It consists of an ontological definition of the domain and the inclusion of real instances coming from both, automatically interpreted and manually labeled documents. The knowledge base has proven to be an effective tool to structure our knowledge and to easily maintain and upgrade it. Moreover, it is an appropriate means to automatically check the consistency of relational data and a convenient complement of hard-coded knowledge interpretation systems.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
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  Area Expedition Conference  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ HRL2017 Serial 3086  
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Author Marçal Rusiñol; Josep Llados edit  openurl
  Title Flowchart Recognition in Patent Information Retrieval Type Book Chapter
  Year (down) 2017 Publication Current Challenges in Patent Information Retrieval Abbreviated Journal  
  Volume 37 Issue Pages 351-368  
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  Abstract  
  Address  
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  Publisher Springer Berlin Heidelberg Place of Publication Editor M. Lupu; K. Mayer; N. Kando; A.J. Trippe  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ RuL2017 Serial 2896  
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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 Conference Article
  Year (down) 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  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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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ IMT2017 Serial 3077  
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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 (down) 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  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  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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