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Author Lluis Gomez; Anguelos Nicolaou; Marçal Rusiñol; Dimosthenis Karatzas edit  openurl
  Title 12 years of ICDAR Robust Reading Competitions: The evolution of reading systems for unconstrained text understanding Type Book Chapter
  Year 2020 Publication Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis Abbreviated Journal  
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  Publisher Springer Place of Publication Editor K. Alahari; C.V. Jawahar  
  Language Summary Language Original Title  
  Series Editor Series Title Series on Advances in Computer Vision and Pattern Recognition Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.121 Approved no  
  Call Number GNR2020 Serial 3494  
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Author Lluis Gomez; Dena Bazazian; Dimosthenis Karatzas edit  openurl
  Title Historical review of scene text detection research Type Book Chapter
  Year 2020 Publication Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
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  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor K. Alahari; C.V. Jawahar  
  Language Summary Language Original Title  
  Series Editor Series Title Series on Advances in Computer Vision and Pattern Recognition Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ GBK2020 Serial 3495  
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Author Jon Almazan; Lluis Gomez; Suman Ghosh; Ernest Valveny; Dimosthenis Karatzas edit  openurl
  Title WATTS: A common representation of word images and strings using embedded attributes for text recognition and retrieval Type Book Chapter
  Year 2020 Publication Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis Abbreviated Journal  
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  Publisher Springer Place of Publication Editor Analysis”, K. Alahari; C.V. Jawahar  
  Language Summary Language Original Title  
  Series Editor Series Title Series on Advances in Computer Vision and Pattern Recognition Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ AGG2020 Serial 3496  
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Author Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe edit   pdf
url  openurl
  Title Retrieval Guided Unsupervised Multi-domain Image to Image Translation Type Conference Article
  Year 2020 Publication 28th ACM International Conference on Multimedia Abbreviated Journal  
  Volume Issue Pages  
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  Abstract Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data.  
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  Area Expedition Conference ACM  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ GLN2020 Serial 3497  
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Author Mohamed Ali Souibgui; Asma Bensalah; Jialuo Chen; Alicia Fornes; Michelle Waldispühl edit  url
doi  openurl
  Title A User Perspective on HTR methods for the Automatic Transcription of Rare Scripts: The Case of Codex Runicus Just Accepted Type Journal Article
  Year 2023 Publication ACM Journal on Computing and Cultural Heritage Abbreviated Journal JOCCH  
  Volume 15 Issue 4 Pages 1-18  
  Keywords  
  Abstract Recent breakthroughs in Artificial Intelligence, Deep Learning and Document Image Analysis and Recognition have significantly eased the creation of digital libraries and the transcription of historical documents. However, for documents in rare scripts with few labelled training data available, current Handwritten Text Recognition (HTR) systems are too constraint. Moreover, research on HTR often focuses on technical aspects only, and rarely puts emphasis on implementing software tools for scholars in Humanities. In this article, we describe, compare and analyse different transcription methods for rare scripts. We evaluate their performance in a real use case of a medieval manuscript written in the runic script (Codex Runicus) and discuss advantages and disadvantages of each method from the user perspective. From this exhaustive analysis and comparison with a fully manual transcription, we raise conclusions and provide recommendations to scholars interested in using automatic transcription tools.  
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  Publisher ACM 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  
  Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ SBC2023 Serial 3732  
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Author Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes edit   pdf
url  openurl
  Title Learning Graph Edit Distance by Graph NeuralNetworks Type Miscellaneous
  Year 2020 Publication Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset.  
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  Area Expedition Conference  
  Notes DAG; 600.121; 600.140; 601.302 Approved no  
  Call Number Admin @ si @ RFL2020 Serial 3555  
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Author Minesh Mathew; Ruben Tito; Dimosthenis Karatzas; R.Manmatha; C.V. Jawahar edit   pdf
url  openurl
  Title Document Visual Question Answering Challenge 2020 Type Conference Article
  Year 2020 Publication 33rd IEEE Conference on Computer Vision and Pattern Recognition – Short paper Abbreviated Journal  
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  Abstract This paper presents results of Document Visual Question Answering Challenge organized as part of “Text and Documents in the Deep Learning Era” workshop, in CVPR 2020. The challenge introduces a new problem – Visual Question Answering on document images. The challenge comprised two tasks. The first task concerns with asking questions on a single document image. On the other hand, the second task is set as a retrieval task where the question is posed over a collection of images. For the task 1 a new dataset is introduced comprising 50,000 questions-answer(s) pairs defined over 12,767 document images. For task 2 another dataset has been created comprising 20 questions over 14,362 document images which share the same document template.  
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  ISSN ISBN Medium  
  Area Expedition Conference CVPR  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ MTK2020 Serial 3558  
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Author Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal edit   pdf
url  doi
openurl 
  Title Beyond Document Object Detection: Instance-Level Segmentation of Complex Layouts Type Journal Article
  Year 2021 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 24 Issue Pages 269–281  
  Keywords  
  Abstract Information extraction is a fundamental task of many business intelligence services that entail massive document processing. Understanding a document page structure in terms of its layout provides contextual support which is helpful in the semantic interpretation of the document terms. In this paper, inspired by the progress of deep learning methodologies applied to the task of object recognition, we transfer these models to the specific case of document object detection, reformulating the traditional problem of document layout analysis. Moreover, we importantly contribute to prior arts by defining the task of instance segmentation on the document image domain. An instance segmentation paradigm is especially important in complex layouts whose contents should interact for the proper rendering of the page, i.e., the proper text wrapping around an image. Finally, we provide an extensive evaluation, both qualitative and quantitative, that demonstrates the superior performance of the proposed methodology over the current state of the art.  
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  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes DAG; 600.121; 600.140; 110.312 Approved no  
  Call Number Admin @ si @ BRL2021b Serial 3574  
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Author Debora Gil; Oriol Ramos Terrades; Raquel Perez edit  doi
openurl 
  Title Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution Type Book Chapter
  Year 2021 Publication Extended Abstracts GEOMVAP 2019, Trends in Mathematics 15 Abbreviated Journal  
  Volume 15 Issue Pages 89–93  
  Keywords  
  Abstract Abnormalities in radiomic measures correlate to genomic alterations prone to alter the outcome of personalized anti-cancer treatments. TOPiomics is a new method for the early detection of variations in tumor imaging phenotype from a topological structure in multi-view radiomic spaces.  
  Address (down)  
  Corporate Author Thesis  
  Publisher Springer Nature 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  
  Notes IAM; DAG; 600.120; 600.145; 600.139 Approved no  
  Call Number Admin @ si @ GRP2021 Serial 3594  
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Author Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes edit   pdf
url  openurl
  Title Learning graph edit distance by graph neural networks Type Journal Article
  Year 2021 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 120 Issue Pages 108132  
  Keywords  
  Abstract The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words i.e. keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset.  
  Address (down)  
  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  
  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ RFL2021 Serial 3611  
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