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Author (up) Lluis Gomez; Dimosthenis Karatzas edit   pdf
doi  openurl
  Title MSER-based Real-Time Text Detection and Tracking Type Conference Article
  Year 2014 Publication 22nd International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 3110 - 3115  
  Keywords  
  Abstract We present a hybrid algorithm for detection and tracking of text in natural scenes that goes beyond the fulldetection approaches in terms of time performance optimization.
A state-of-the-art scene text detection module based on Maximally Stable Extremal Regions (MSER) is used to detect text asynchronously, while on a separate thread detected text objects are tracked by MSER propagation. The cooperation of these two modules yields real time video processing at high frame rates even on low-resource devices.
 
  Address Stockholm; August 2014  
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  ISSN 1051-4651 ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG; 600.056; 601.158; 601.197; 600.077 Approved no  
  Call Number Admin @ si @ GoK2014a Serial 2492  
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Author (up) Lluis Gomez; Dimosthenis Karatzas edit  openurl
  Title Scene Text Recognition: No Country for Old Men? Type Conference Article
  Year 2014 Publication 1st International Workshop on Robust Reading Abbreviated Journal  
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  Area Expedition Conference IWRR  
  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ GoK2014c Serial 2538  
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Author (up) Lluis Gomez; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Object Proposals for Text Extraction in the Wild Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 206 - 210  
  Keywords  
  Abstract Object Proposals is a recent computer vision technique receiving increasing interest from the research community. Its main objective is to generate a relatively small set of bounding box proposals that are most likely to contain objects of interest. The use of Object Proposals techniques in the scene text understanding field is innovative. Motivated by the success of powerful while expensive techniques to recognize words in a holistic way, Object Proposals techniques emerge as an alternative to the traditional text detectors. In this paper we study to what extent the existing generic Object Proposals methods may be useful for scene text understanding. Also, we propose a new Object Proposals algorithm that is specifically designed for text and compare it with other generic methods in the state of the art. Experiments show that our proposal is superior in its ability of producing good quality word proposals in an efficient way. The source code of our method is made publicly available  
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  Notes DAG; 600.077; 600.084; 601.197 Approved no  
  Call Number Admin @ si @ GoK2015 Serial 2691  
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Author (up) Lluis Gomez; Dimosthenis Karatzas edit   pdf
url  openurl
  Title A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction Type Journal Article
  Year 2016 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 19 Issue 4 Pages 335-349  
  Keywords scene text; segmentation; detection; hierarchical grouping; perceptual organisation  
  Abstract Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective.
Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with
high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art
methods in unconstrained scenarios.
 
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  Notes DAG; 600.056; 601.197 Approved no  
  Call Number Admin @ si @ GoK2016a Serial 2862  
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Author (up) Lluis Gomez; Dimosthenis Karatzas edit   pdf
doi  openurl
  Title A fine-grained approach to scene text script identification Type Conference Article
  Year 2016 Publication 12th IAPR Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 192-197  
  Keywords  
  Abstract This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online.  
  Address Santorini; Grecia; April 2016  
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  Notes DAG; 601.197; 600.084 Approved no  
  Call Number Admin @ si @ GoK2016b Serial 2863  
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Author (up) 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 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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  Notes DAG; 600.084; 601.197; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ GoK2017 Serial 2886  
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Author (up) Lluis Gomez; Marçal Rusiñol; Ali Furkan Biten; Dimosthenis Karatzas edit   pdf
openurl 
  Title Subtitulació automàtica d'imatges. Estat de l'art i limitacions en el context arxivístic Type Conference Article
  Year 2018 Publication Jornades Imatge i Recerca Abbreviated Journal  
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  Area Expedition Conference JIR  
  Notes DAG; 600.084; 600.135; 601.338; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ GRB2018 Serial 3173  
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Author (up) 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  
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ GRK2017 Serial 2999  
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Author (up) Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Cutting Sayre's Knot: Reading Scene Text without Segmentation. Application to Utility Meters Type Conference Article
  Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 97-102  
  Keywords Robust Reading; End-to-end Systems; CNN; Utility Meters  
  Abstract In this paper we present a segmentation-free system for reading text in natural scenes. A CNN architecture is trained in an end-to-end manner, and is able to directly output readings without any explicit text localization step. In order to validate our proposal, we focus on the specific case of reading utility meters. We present our results in a large dataset of images acquired by different users and devices, so text appears in any location, with different sizes, fonts and lengths, and the images present several distortions such as
dirt, illumination highlights or blur.
 
  Address Viena; Austria; April 2018  
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  Area Expedition Conference DAS  
  Notes DAG; 600.084; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ GRK2018 Serial 3102  
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Author (up) 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 2017 Publication 30th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages  
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  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  
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  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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