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Author Lei Kang; Juan Ignacio Toledo; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol edit   pdf
url  openurl
  Title Convolve, Attend and Spell: An Attention-based Sequence-to-Sequence Model for Handwritten Word Recognition Type Conference Article
  Year 2018 Publication 40th German Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 459-472  
  Keywords  
  Abstract This paper proposes Convolve, Attend and Spell, an attention based sequence-to-sequence model for handwritten word recognition. The proposed architecture has three main parts: an encoder, consisting of a CNN and a bi-directional GRU, an attention mechanism devoted to focus on the pertinent features and a decoder formed by a one-directional GRU, able to spell the corresponding word, character by character. Compared with the recent state-of-the-art, our model achieves competitive results on the IAM dataset without needing any pre-processing step, predefined lexicon nor language model. Code and additional results are available in https://github.com/omni-us/research-seq2seq-HTR.  
  Address Stuttgart; Germany; October 2018  
  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 GCPR  
  Notes (up) DAG; 600.097; 603.057; 302.065; 601.302; 600.084; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ KTR2018 Serial 3167  
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Author Jialuo Chen; Pau Riba; Alicia Fornes; Juan Mas; Josep Llados; Joana Maria Pujadas-Mora edit   pdf
doi  openurl
  Title Word-Hunter: A Gamesourcing Experience to Validate the Transcription of Historical Manuscripts Type Conference Article
  Year 2018 Publication 16th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal  
  Volume Issue Pages 528-533  
  Keywords Crowdsourcing; Gamification; Handwritten documents; Performance evaluation  
  Abstract Nowadays, there are still many handwritten historical documents in archives waiting to be transcribed and indexed. Since manual transcription is tedious and time consuming, the automatic transcription seems the path to follow. However, the performance of current handwriting recognition techniques is not perfect, so a manual validation is mandatory. Crowdsourcing is a good strategy for manual validation, however it is a tedious task. In this paper we analyze experiences based in gamification
in order to propose and design a gamesourcing framework that increases the interest of users. Then, we describe and analyze our experience when validating the automatic transcription using the gamesourcing application. Moreover, thanks to the combination of clustering and handwriting recognition techniques, we can speed up the validation while maintaining the performance.
 
  Address Niagara Falls, USA; August 2018  
  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 ICFHR  
  Notes (up) DAG; 600.097; 603.057; 600.121 Approved no  
  Call Number Admin @ si @ CRF2018 Serial 3169  
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Author Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes edit   pdf
doi  openurl
  Title Learning Graph Distances with Message Passing Neural Networks Type Conference Article
  Year 2018 Publication 24th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 2239-2244  
  Keywords ★Best Paper Award★  
  Abstract Graph representations have been widely used in pattern recognition thanks to their powerful representation formalism and rich theoretical background. A number of error-tolerant graph matching algorithms such as graph edit distance have been proposed for computing a distance between two labelled graphs. However, they typically suffer from a high
computational complexity, which makes it difficult to apply
these matching algorithms in a real scenario. In this paper, we propose an efficient graph distance based on the emerging field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure and learns a metric with a siamese network approach. The performance of the proposed graph distance is validated in two application cases, graph classification and graph retrieval of handwritten words, and shows a promising performance when compared with
(approximate) graph edit distance benchmarks.
 
  Address Beijing; China; August 2018  
  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 ICPR  
  Notes (up) DAG; 600.097; 603.057; 601.302; 600.121 Approved no  
  Call Number Admin @ si @ RFL2018 Serial 3168  
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Author Manuel Carbonell; Mauricio Villegas; Alicia Fornes; Josep Llados edit   pdf
openurl 
  Title Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model Type Conference Article
  Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 399-404  
  Keywords Named entity recognition; Handwritten Text Recognition; neural networks  
  Abstract When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different
configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing.
 
  Address Vienna; Austria; April 2018  
  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 (up) DAG; 600.097; 603.057; 601.311; 600.121 Approved no  
  Call Number Admin @ si @ CVF2018 Serial 3170  
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Author Dena Bazazian edit  isbn
openurl 
  Title Fully Convolutional Networks for Text Understanding in Scene Images Type Book Whole
  Year 2018 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract 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.
 
  Address November 2018  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Dimosthenis Karatzas;Andrew Bagdanov  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-948531-1-1 Medium  
  Area Expedition Conference  
  Notes (up) DAG; 600.121 Approved no  
  Call Number Admin @ si @ Baz2018 Serial 3220  
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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 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 (up) DAG; 600.121 Approved no  
  Call Number Admin @ si @ NYB2017 Serial 3097  
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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 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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  Abstract  
  Address  
  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 978-84-697-5692-8 Medium  
  Area Expedition Conference HDH  
  Notes (up) DAG; 600.121 Approved no  
  Call Number Admin @ si @ VFV2017 Serial 3060  
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Author Raul Gomez; Baoguang Shi; Lluis Gomez; Lukas Numann; Andreas Veit; Jiri Matas; Serge Belongie; Dimosthenis Karatzas edit  doi
openurl 
  Title ICDAR2017 Robust Reading Challenge on COCO-Text Type Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  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 (up) DAG; 600.121 Approved no  
  Call Number Admin @ si @ GSG2017 Serial 3076  
Permanent link to this record
 

 
Author Suman Ghosh; Ernest Valveny edit   pdf
doi  openurl
  Title R-PHOC: Segmentation-Free Word Spotting using CNN Type 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.
 
  Address  
  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 ICDAR  
  Notes (up) DAG; 600.121 Approved no  
  Call Number Admin @ si @ GhV2017a Serial 3079  
Permanent link to this record
 

 
Author Suman Ghosh; Ernest Valveny edit   pdf
doi  openurl
  Title Visual attention models for scene text recognition Type Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  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.
 
  Address  
  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 ICDAR  
  Notes (up) DAG; 600.121 Approved no  
  Call Number Admin @ si @ GhV2017b Serial 3080  
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