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Author 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  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  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 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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  Series Editor Series Title Abbreviated Series Title (up)  
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  ISSN ISBN Medium  
  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 Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol; Anguelos Nicolaou edit   pdf
url  openurl
  Title The Robust Reading Competition Annotation and Evaluation Platform Type Conference Article
  Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 61-66  
  Keywords  
  Abstract The ICDAR Robust Reading Competition (RRC), initiated in 2003 and reestablished in 2011, has become the defacto evaluation standard for the international community. Concurrent with its second incarnation in 2011, a continuous
effort started to develop an online framework to facilitate the hosting and management of competitions. This short paper briefly outlines the Robust Reading Competition Annotation and Evaluation Platform, the backbone of the
Robust Reading Competition, comprising a collection of tools and processes that aim to simplify the management and annotation of data, and to provide online and offline performance evaluation and analysis services.
 
  Address Viena; Austria; April 2018  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference DAS  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number KGR2018 Serial 3103  
Permanent link to this record
 

 
Author David Aldavert; Marçal Rusiñol edit   pdf
doi  openurl
  Title Manuscript text line detection and segmentation using second-order derivatives analysis Type Conference Article
  Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 293 - 298  
  Keywords text line detection; text line segmentation; text region detection; second-order derivatives  
  Abstract In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a
bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets.
 
  Address Viena; Austria; April 2018  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference DAS  
  Notes DAG; 600.084; 600.129; 302.065; 600.121 Approved no  
  Call Number Admin @ si @ AlR2018a Serial 3104  
Permanent link to this record
 

 
Author David Aldavert; Marçal Rusiñol edit   pdf
doi  openurl
  Title Synthetically generated semantic codebook for Bag-of-Visual-Words based word spotting Type Conference Article
  Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 223 - 228  
  Keywords Word Spotting; Bag of Visual Words; Synthetic Codebook; Semantic Information  
  Abstract Word-spotting methods based on the Bag-ofVisual-Words framework have demonstrated a good retrieval performance even when used in a completely unsupervised manner. Although unsupervised approaches are suitable for
large document collections due to the cost of acquiring labeled data, these methods also present some drawbacks. For instance, having to train a suitable “codebook” for a certain dataset has a high computational cost. Therefore, in
this paper we present a database agnostic codebook which is trained from synthetic data. The aim of the proposed approach is to generate a codebook where the only information required is the type of script used in the document. The use of synthetic data also allows to easily incorporate semantic
information in the codebook generation. So, the proposed method is able to determine which set of codewords have a semantic representation of the descriptor feature space. Experimental results show that the resulting codebook attains a state-of-the-art performance while having a more compact representation.
 
  Address Viena; Austria; April 2018  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference DAS  
  Notes DAG; 600.084; 600.129; 600.121 Approved no  
  Call Number Admin @ si @ AlR2018b Serial 3105  
Permanent link to this record
 

 
Author V. Poulain d'Andecy; Emmanuel Hartmann; Marçal Rusiñol edit   pdf
doi  openurl
  Title Field Extraction by hybrid incremental and a-priori structural templates Type Conference Article
  Year 2018 Publication 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 251 - 256  
  Keywords Layout Analysis; information extraction; incremental learning  
  Abstract In this paper, we present an incremental framework for extracting information fields from administrative documents. First, we demonstrate some limits of the existing state-of-the-art methods such as the delay of the system efficiency. This is a concern in industrial context when we have only few samples of each document class. Based on this analysis, we propose a hybrid system combining incremental learning by means of itf-df statistics and a-priori generic
models. We report in the experimental section our results obtained with a dataset of real invoices.
 
  Address Viena; Austria; April 2018  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference DAS  
  Notes DAG; 600.084; 600.129; 600.121 Approved no  
  Call Number Admin @ si @ PHR2018 Serial 3106  
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Author Mohammed Al Rawi; Dimosthenis Karatzas edit   pdf
openurl 
  Title On the Labeling Correctness in Computer Vision Datasets Type Conference Article
  Year 2018 Publication Proceedings of the Workshop on Interactive Adaptive Learning, co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Image datasets have heavily been used to build computer vision systems.
These datasets are either manually or automatically labeled, which is a
problem as both labeling methods are prone to errors. To investigate this problem, we use a majority voting ensemble that combines the results from several Convolutional Neural Networks (CNNs). Majority voting ensembles not only enhance the overall performance, but can also be used to estimate the confidence level of each sample. We also examined Softmax as another form to estimate posterior probability. We have designed various experiments with a range of different ensembles built from one or different, or temporal/snapshot CNNs, which have been trained multiple times stochastically. We analyzed CIFAR10, CIFAR100, EMNIST, and SVHN datasets and we found quite a few incorrect
labels, both in the training and testing sets. We also present detailed confidence analysis on these datasets and we found that the ensemble is better than the Softmax when used estimate the per-sample confidence. This work thus proposes an approach that can be used to scrutinize and verify the labeling of computer vision datasets, which can later be applied to weakly/semi-supervised learning. We propose a measure, based on the Odds-Ratio, to quantify how many of these incorrectly classified labels are actually incorrectly labeled and how many of these are confusing. The proposed methods are easily scalable to larger datasets, like ImageNet, LSUN and SUN, as each CNN instance is trained for 60 epochs; or even faster, by implementing a temporal (snapshot) ensemble.
 
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  Area Expedition Conference ECML-PKDDW  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ RaK2018 Serial 3144  
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Author Thanh Nam Le; Muhammad Muzzamil Luqman; Anjan Dutta; Pierre Heroux; Christophe Rigaud; Clement Guerin; Pasquale Foggia; Jean Christophe Burie; Jean Marc Ogier; Josep Llados; Sebastien Adam edit  url
openurl 
  Title Subgraph spotting in graph representations of comic book images Type Journal Article
  Year 2018 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 112 Issue Pages 118-124  
  Keywords Attributed graph; Region adjacency graph; Graph matching; Graph isomorphism; Subgraph isomorphism; Subgraph spotting; Graph indexing; Graph retrieval; Query by example; Dataset and comic book images  
  Abstract Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset.  
  Address  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
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  Area Expedition Conference  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ LLD2018 Serial 3150  
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Author Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados edit   pdf
openurl 
  Title Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Framework Type Conference Article
  Year 2018 Publication 14th Asian Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract In this paper we propose an approach for multi-modal image retrieval in multi-labelled images. A multi-modal deep network architecture is formulated to jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features. Both the alignment between the queries and the image and the supervision of the attention on the images are obtained by generalizing the Hungarian Algorithm using different loss functions. This permits encoding the object-based features and its alignment with the query irrespective of the availability of the co-occurrence of different objects in the training set. We validate the performance of our approach on standard single/multi-object datasets, showing state-of-the art performance in every dataset.  
  Address Perth; Australia; December 2018  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ACCV  
  Notes DAG; 600.097; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ DDG2018a Serial 3151  
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Author Sounak Dey; Anjan Dutta; Suman Ghosh; Ernest Valveny; Josep Llados; Umapada Pal edit   pdf
doi  openurl
  Title Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch Type Conference Article
  Year 2018 Publication 24th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 916 - 921  
  Keywords  
  Abstract In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query. A cross-modal deep network architecture is formulated to jointly model the sketch and text input modalities as well as the the image output modality, learning a common embedding between text and images and between sketches and images. In addition, an attention model is used to selectively focus the attention on the different objects of the image, allowing for retrieval with multiple objects in the query. Experiments show that the proposed method performs the best in both single and multiple object image retrieval in standard datasets.  
  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 (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG; 602.167; 602.168; 600.097; 600.084; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ DDG2018b Serial 3152  
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