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Author Sergi Garcia Bordils; Andres Mafla; Ali Furkan Biten; Oren Nuriel; Aviad Aberdam; Shai Mazor; Ron Litman; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Out-of-Vocabulary Challenge Report Type Conference Article
  Year 2022 Publication Proceedings European Conference on Computer Vision Workshops Abbreviated Journal  
  Volume 13804 Issue Pages 359–375  
  Keywords  
  Abstract This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions.  
  Address Tel-Aviv; Israel; October 2022  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECCVW  
  Notes DAG; 600.155; 302.105; 611.002 Approved no  
  Call Number (up) Admin @ si @ GMB2022 Serial 3771  
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Author Lluis Gomez; Andres Mafla; Marçal Rusiñol; Dimosthenis Karatzas edit   pdf
url  openurl
  Title Single Shot Scene Text Retrieval Type Conference Article
  Year 2018 Publication 15th European Conference on Computer Vision Abbreviated Journal  
  Volume 11218 Issue Pages 728-744  
  Keywords Image retrieval; Scene text; Word spotting; Convolutional Neural Networks; Region Proposals Networks; PHOC  
  Abstract Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The novelty of the proposed model consists in the usage of a single shot CNN architecture that predicts at the same time bounding boxes and a compact text representation of the words in them. In this way, the text based image retrieval task can be casted as a simple nearest neighbor search of the query text representation over the outputs of the CNN over the entire image
database. Our experiments demonstrate that the proposed architecture
outperforms previous state-of-the-art while it offers a significant increase
in processing speed.
 
  Address Munich; September 2018  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECCV  
  Notes DAG; 600.084; 601.338; 600.121; 600.129 Approved no  
  Call Number (up) Admin @ si @ GMR2018 Serial 3143  
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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 2017 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 67 Issue Pages 85-96  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.084; 600.121; 600.129 Approved no  
  Call Number (up) Admin @ si @ GNK2017 Serial 2887  
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Author Lluis Gomez; Dimosthenis Karatzas edit   pdf
doi  openurl
  Title Multi-script Text Extraction from Natural Scenes Type Conference Article
  Year 2013 Publication 12th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 467-471  
  Keywords  
  Abstract Scene text extraction methodologies are usually based in classification of individual regions or patches, using a priori knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organisation through which text emerges as a perceptually significant group of atomic objects. Therefore humans are able to detect text even in languages and scripts never seen before. In this paper, we argue that the text extraction problem could be posed as the detection of meaningful groups of regions. We present a method built around a perceptual organisation framework that exploits collaboration of proximity and similarity laws to create text-group hypotheses. Experiments demonstrate that our algorithm is competitive with state of the art approaches on a standard dataset covering text in variable orientations and two languages.  
  Address Washington; USA; August 2013  
  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 1520-5363 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.056; 601.158; 601.197 Approved no  
  Call Number (up) Admin @ si @ GoK2013 Serial 2310  
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Author 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  
  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 1051-4651 ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG; 600.056; 601.158; 601.197; 600.077 Approved no  
  Call Number (up) Admin @ si @ GoK2014a Serial 2492  
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Author 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  
  Volume Issue Pages  
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  ISSN ISBN Medium  
  Area Expedition Conference IWRR  
  Notes DAG; 600.077 Approved no  
  Call Number (up) Admin @ si @ GoK2014c Serial 2538  
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Author 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  
  Address  
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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; 600.077; 600.084; 601.197 Approved no  
  Call Number (up) Admin @ si @ GoK2015 Serial 2691  
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Author 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.
 
  Address  
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  Notes DAG; 600.056; 601.197 Approved no  
  Call Number (up) Admin @ si @ GoK2016a Serial 2862  
Permanent link to this record
 

 
Author 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  
  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 DAS  
  Notes DAG; 601.197; 600.084 Approved no  
  Call Number (up) Admin @ si @ GoK2016b Serial 2863  
Permanent link to this record
 

 
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 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 (up) Admin @ si @ GoK2017 Serial 2886  
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