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Author Josep Llados; Daniel Lopresti; Seiichi Uchida (eds)
Title 16th International Conference, 2021, Proceedings, Part II Type Book Whole
Year 2021 Publication Document Analysis and Recognition – ICDAR 2021 Abbreviated Journal
Volume 12822 Issue Pages
Keywords
Abstract This four-volume set of LNCS 12821, LNCS 12822, LNCS 12823 and LNCS 12824, constitutes the refereed proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, held in Lausanne, Switzerland in September 2021. The 182 full papers were carefully reviewed and selected from 340 submissions, and are presented with 13 competition reports.

The papers are organized into the following topical sections: document analysis for literature search, document summarization and translation, multimedia document analysis, mobile text recognition, document analysis for social good, indexing and retrieval of documents, physical and logical layout analysis, recognition of tables and formulas, and natural language processing (NLP) for document understanding.
Address (up) Lausanne, Switzerland, September 5-10, 2021
Corporate Author Thesis
Publisher Springer Cham Place of Publication Editor Josep Llados; Daniel Lopresti; Seiichi Uchida
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-030-86330-2 Medium
Area Expedition Conference ICDAR
Notes DAG Approved no
Call Number Admin @ si @ Serial 3726
Permanent link to this record
 
 
Author Adria Molina; Pau Riba; Lluis Gomez; Oriol Ramos Terrades; Josep Llados
Title Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach Type Conference Article
Year 2021 Publication 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12822 Issue Pages 306-320
Keywords
Abstract This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods.
Address (up) Lausanne; Suissa; September 2021
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 ICDAR
Notes DAG; 600.121; 600.140; 110.312 Approved no
Call Number Admin @ si @ MRG2021b Serial 3571
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Author Pau Riba; Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados
Title Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting Type Conference Article
Year 2021 Publication 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12822 Issue Pages 381–395
Keywords
Abstract In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work.
Address (up) Lausanne; Suissa; September 2021
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; 600.121; 600.140; 110.312 Approved no
Call Number Admin @ si @ RMG2021 Serial 3572
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Author Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal
Title DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis Type Conference Article
Year 2021 Publication 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12823 Issue Pages 555–568
Keywords
Abstract Despite significant progress on current state-of-the-art image generation models, synthesis of document images containing multiple and complex object layouts is a challenging task. This paper presents a novel approach, called DocSynth, to automatically synthesize document images based on a given layout. In this work, given a spatial layout (bounding boxes with object categories) as a reference by the user, our proposed DocSynth model learns to generate a set of realistic document images consistent with the defined layout. Also, this framework has been adapted to this work as a superior baseline model for creating synthetic document image datasets for augmenting real data during training for document layout analysis tasks. Different sets of learning objectives have been also used to improve the model performance. Quantitatively, we also compare the generated results of our model with real data using standard evaluation metrics. The results highlight that our model can successfully generate realistic and diverse document images with multiple objects. We also present a comprehensive qualitative analysis summary of the different scopes of synthetic image generation tasks. Lastly, to our knowledge this is the first work of its kind.
Address (up) Lausanne; Suissa; September 2021
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
Notes DAG; 600.121; 600.140; 110.312 Approved no
Call Number Admin @ si @ BRL2021a Serial 3573
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Author Albert Suso; Pau Riba; Oriol Ramos Terrades; Josep Llados
Title A Self-supervised Inverse Graphics Approach for Sketch Parametrization Type Conference Article
Year 2021 Publication 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12916 Issue Pages 28-42
Keywords
Abstract The study of neural generative models of handwritten text and human sketches is a hot topic in the computer vision field. The landmark SketchRNN provided a breakthrough by sequentially generating sketches as a sequence of waypoints, and more recent articles have managed to generate fully vector sketches by coding the strokes as Bézier curves. However, the previous attempts with this approach need them all a ground truth consisting in the sequence of points that make up each stroke, which seriously limits the datasets the model is able to train in. In this work, we present a self-supervised end-to-end inverse graphics approach that learns to embed each image to its best fit of Bézier curves. The self-supervised nature of the training process allows us to train the model in a wider range of datasets, but also to perform better after-training predictions by applying an overfitting process on the input binary image. We report qualitative an quantitative evaluations on the MNIST and the Quick, Draw! datasets.
Address (up) Lausanne; Suissa; September 2021
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 ICDAR
Notes DAG; 600.121 Approved no
Call Number Admin @ si @ SRR2021 Serial 3675
Permanent link to this record
 
 
Author Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal
Title Graph-Based Deep Generative Modelling for Document Layout Generation Type Conference Article
Year 2021 Publication 16th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 12917 Issue Pages 525-537
Keywords
Abstract One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices.
Address (up) Lausanne; Suissa; September 2021
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
Notes DAG; 600.121; 600.140; 110.312 Approved no
Call Number Admin @ si @ BRL2021 Serial 3676
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Author Josep Llados
Title The 5G of Document Intelligence Type Conference Article
Year 2021 Publication 3rd Workshop on Future of Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address (up) Lausanne; Suissa; September 2021
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 FDAR
Notes DAG Approved no
Call Number Admin @ si @ Serial 3677
Permanent link to this record
 
 
Author Adriana Romero; Carlo Gatta; Gustavo Camps-Valls
Title Unsupervised Deep Feature Extraction Of Hyperspectral Images Type Conference Article
Year 2014 Publication 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Abbreviated Journal
Volume Issue Pages
Keywords Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification
Abstract This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms the previous state-ofthe-art results on the same experimental setting. Results show that single layer networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels. Regarding the deep architecture, we can conclude that: (1) additional layers in a deep architecture significantly improve the performance w.r.t. single layer variants; (2) the max-pooling step in each layer is mandatory to achieve satisfactory results; and (3) the performance gain w.r.t. the number of layers is upper bounded, since the spatial resolution is reduced at each pooling, resulting in too spatially coarse output features.
Address (up) Lausanne; Switzerland; June 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 ISBN Medium
Area Expedition Conference WHISPERS
Notes MILAB; LAMP; 600.079 Approved no
Call Number Admin @ si @ RGC2014 Serial 2513
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Author Pau Baiget; Eric Sommerlade; I. Reid; Jordi Gonzalez
Title Finding Prototypes to Estimate Trajectory Development in Outdoor Scenarios Type Conference Article
Year 2008 Publication First International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences BMVC 2008, Abbreviated Journal
Volume Issue Pages 27–34
Keywords
Abstract
Address (up) Leed
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 978-84-935251-9-4 Medium
Area Expedition Conference THEMIS’
Notes ISE Approved no
Call Number ISE @ ise @ BSR2008 Serial 1008
Permanent link to this record
 
 
Author Jordi Gonzalez; Thomas B. Moeslund
Title Tracking Humans for the Evaluation of their Motion in Image Sequences Type Book Whole
Year 2008 Publication First International Workshop, THEMIS Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address (up) Leeds (UK)
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 978-84-935251-9-4 Medium
Area Expedition Conference THEMIS
Notes Approved no
Call Number ISE @ ise @ GMW2008 Serial 1002
Permanent link to this record
 
 
Author Ognjen Rudovic; Xavier Roca
Title Building Temporale Templates for Human Behaviour Classification Type Conference Article
Year 2008 Publication First International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences BMVC 2008, Abbreviated Journal
Volume Issue Pages 79–88
Keywords
Abstract
Address (up) Leeds (UK)
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 978-84-935251-9-4 Medium
Area Expedition Conference THEMIS’
Notes ISE Approved no
Call Number ISE @ ise @ RuR2008 Serial 1009
Permanent link to this record
 
 
Author Carles Fernandez; Pau Baiget; Jordi Gonzalez
Title Cognitive-Guided Semantic Exploitation in Video Surveillance Interfaces Type Conference Article
Year 2008 Publication First International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences. BMVC 2008, Abbreviated Journal
Volume Issue Pages 53–60
Keywords
Abstract
Address (up) Leeds (UK)
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 978-84-935251-9-4 Medium
Area Expedition Conference THEMIS’
Notes ISE Approved no
Call Number ISE @ ise @ FBG2008 Serial 1010
Permanent link to this record
 
 
Author Craig Von Land; Ricardo Toledo; Juan J. Villanueva
Title Object Oriented Design of the DICOM standard Type Miscellaneous
Year 1996 Publication International Symposium on Cardiovascular Imaging. Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address (up) Leiden, The Netherlands
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 ADAS Approved no
Call Number ISE @ ise @ VTV1996c Serial 104
Permanent link to this record
 
 
Author C. Alejandro Parraga; Xavier Otazu; Arash Akbarinia
Title Modelling symmetry perception with banks of quadrature convolutional Gabor kernels Type Conference Article
Year 2019 Publication 42nd edition of the European Conference on Visual Perception Abbreviated Journal
Volume Issue Pages 224-224
Keywords
Abstract Mirror symmetry is a property most likely to be encountered in animals than in medium scale vegetation or inanimate objects in the natural world. This might be the reason why the human visual system has evolved to detect it quickly and robustly. Indeed, the perception of symmetry assists higher-level visual processing that are crucial for survival such as target recognition and identification irrespective of position and location. Although the task of detecting symmetrical objects seems effortless to us, it is very challenging for computers (to the extent that it has been proposed as a robust “captcha” by Funk & Liu in 2016). Indeed, the exact mechanism of symmetry detection in primates is not well understood: fMRI studies have shown that symmetrical shapes activate specific higher-level areas of the visual cortex (Sasaki et al.; 2005) and similarly, a large body of psychophysical experiments suggest that the symmetry perception is critically influenced by low-level mechanisms (Treder; 2010). In this work we attempt to find plausible low-level mechanisms that might form the basis for symmetry perception. Our simple model is made from banks of (i) odd-symmetric Gabors (resembling edge-detecting V1 neurons); and (ii) banks of larger odd- and even-symmetric Gabors (resembling higher visual cortex neurons), that pool signals from the 'edge image'. As reported previously (Akbarinia et al, ECVP2017), the convolution of the symmetrical lines with the two Gabor kernels of alternative phase produces a minimum in one and a maximum in the other (Osorio; 1996), and the rectification and combination of these signals create lines which hint of mirror symmetry in natural images. We improved the algorithm by combining these signals across several spatial scales. Our preliminary results suggest that such multiscale combination of convolutional operations might form the basis for much of the operation of the HVS in terms of symmetry detection and representation.
Address (up) Leuven; Belgium; August 2019
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 ECVP
Notes NEUROBIT; 600.128 Approved no
Call Number Admin @ si @ POA2019 Serial 3371
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Author Isabelle Guyon; Kristin Bennett; Gavin Cawley; Hugo Jair Escalante; Sergio Escalera; Tin Kam Ho; Nuria Macia; Bisakha Ray; Mehreen Saeed; Alexander Statnikov; Evelyne Viegas
Title AutoML Challenge 2015: Design and First Results Type Conference Article
Year 2015 Publication 32nd International Conference on Machine Learning, ICML workshop, JMLR proceedings ICML15 Abbreviated Journal
Volume Issue Pages 1-8
Keywords AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning
Abstract ChaLearn is organizing the Automatic Machine Learning (AutoML) contest 2015, which challenges participants to solve classi cation and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction results only. Datasets of progressively increasing diculty are introduced throughout the six rounds of the challenge. (Participants can
enter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen (AutoML), and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance (Tweakathon). This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform will remain available beyond the termination of the challenge: http://codalab.org/AutoML.
Address (up) Lille; France; July 2015
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 ICML
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ GBC2015c Serial 2656
Permanent link to this record