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Author Arka Ujal Dey; Suman Ghosh; Ernest Valveny; Gaurav Harit edit   pdf
url  doi
openurl 
  Title Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding Type Journal Article
  Year 2021 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 149 Issue Pages 164-171  
  Keywords  
  Abstract Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art.  
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  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ DGV2021 Serial (down) 3364  
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Author Ekta Vats; Anders Hast; Alicia Fornes edit   pdf
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openurl 
  Title Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion Type Conference Article
  Year 2019 Publication 15th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 1294-1299  
  Keywords Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching  
  Abstract Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors
and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method.
 
  Address Sydney; Australia; September 2019  
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ VHF2019 Serial (down) 3356  
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Author Pau Riba; Anjan Dutta; Lutz Goldmann; Alicia Fornes; Oriol Ramos Terrades; Josep Llados edit   pdf
url  doi
openurl 
  Title Table Detection in Invoice Documents by Graph Neural Networks Type Conference Article
  Year 2019 Publication 15th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 122-127  
  Keywords  
  Abstract Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical or quantitative relationships among pieces of information. In digital mail room applications, where a large amount of
administrative documents must be processed with reasonable accuracy, the detection and interpretation of tables is crucial. Table recognition has gained interest in document image analysis, in particular in unconstrained formats (absence of rule lines, unknown information of rows and columns). In this work, we propose a graph-based approach for detecting tables in document images. Instead of using the raw content (recognized text), we make use of the location, context and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text
reading. Our framework makes use of Graph Neural Networks (GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model has been experimentally validated in two invoice datasets and achieved encouraging results. Additionally, due to the scarcity
of benchmark datasets for this task, we have contributed to the community a novel dataset derived from the RVL-CDIP invoice data. It will be publicly released to facilitate future research.
 
  Address Sydney; Australia; September 2019  
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.140; 601.302; 602.167; 600.121; 600.141 Approved no  
  Call Number Admin @ si @ RDG2019 Serial (down) 3355  
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Author Asma Bensalah; Pau Riba; Alicia Fornes; Josep Llados edit   pdf
openurl 
  Title Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning Type Conference Article
  Year 2019 Publication 13th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages 80-85  
  Keywords Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning  
  Abstract With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,
which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of
sketches, showing promising results.
 
  Address Sydney; Australia; September 2019  
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  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference GREC  
  Notes DAG; 600.140; 601.302; 600.121 Approved no  
  Call Number Admin @ si @ BRF2019 Serial (down) 3354  
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Author Manuel Carbonell; Joan Mas; Mauricio Villegas; Alicia Fornes; Josep Llados edit   pdf
url  doi
openurl 
  Title End-to-End Handwritten Text Detection and Transcription in Full Pages Type Conference Article
  Year 2019 Publication 2nd International Workshop on Machine Learning Abbreviated Journal  
  Volume 5 Issue Pages 29-34  
  Keywords Handwritten Text Recognition; Layout Analysis; Text segmentation; Deep Neural Networks; Multi-task learning  
  Abstract When transcribing handwritten document images, inaccuracies in the text segmentation step often cause errors in the subsequent transcription step. For this reason, some recent methods propose to perform the recognition at paragraph level. But still, errors in the segmentation of paragraphs can affect
the transcription performance. In this work, we propose an end-to-end framework to transcribe full pages. The joint text detection and transcription allows to remove the layout analysis requirement at test time. The experimental results show that our approach can achieve comparable results to models that assume
segmented paragraphs, and suggest that joining the two tasks brings an improvement over doing the two tasks separately.
 
  Address Sydney; Australia; September 2019  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference ICDAR WML  
  Notes DAG; 600.140; 601.311; 600.140 Approved no  
  Call Number Admin @ si @ CMV2019 Serial (down) 3353  
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Author Veronica Romero; Emilio Granell; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez edit   pdf
url  openurl
  Title Information Extraction in Handwritten Marriage Licenses Books Type Conference Article
  Year 2019 Publication 5th International Workshop on Historical Document Imaging and Processing Abbreviated Journal  
  Volume Issue Pages 66-71  
  Keywords  
  Abstract Handwritten marriage licenses books are characterized by a simple structure of the text in the records with an evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. Previous works have shown that the use of category-based language models and a Grammatical Inference technique known as MGGI can improve the accuracy of these
tasks. However, the application of the MGGI algorithm requires an a priori knowledge to label the words of the training strings, that is not always easy to obtain. In this paper we study how to automatically obtain the information required by the MGGI algorithm using a technique based on Confusion Networks. Using the resulting language model, full handwritten text recognition and information extraction experiments have been carried out with results supporting the proposed approach.
 
  Address Sydney; Australia; September 2019  
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  Area Expedition Conference HIP  
  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ RGF2019 Serial (down) 3352  
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Author Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Gabriel Brea-Martinez; Miquel Valls-Figols edit  url
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  Title The Baix Llobregat (BALL) Demographic Database, between Historical Demography and Computer Vision (nineteenth–twentieth centuries Type Book Chapter
  Year 2019 Publication Nominative Data in Demographic Research in the East and the West: monograph Abbreviated Journal  
  Volume Issue Pages 29-61  
  Keywords  
  Abstract The Baix Llobregat (BALL) Demographic Database is an ongoing database project containing individual census data from the Catalan region of Baix Llobregat (Spain) during the nineteenth and twentieth centuries. The BALL Database is built within the project ‘NETWORKS: Technology and citizen innovation for building historical social networks to understand the demographic past’ directed by Alícia Fornés from the Center for Computer Vision and Joana Maria Pujadas-Mora from the Center for Demographic Studies, both at the Universitat Autònoma de Barcelona, funded by the Recercaixa program (2017–2019).
Its webpage is http://dag.cvc.uab.es/xarxes/.The aim of the project is to develop technologies facilitating massive digitalization of demographic sources, and more specifically the padrones (local censuses), in order to reconstruct historical ‘social’ networks employing computer vision technology. Such virtual networks can be created thanks to the linkage of nominative records compiled in the local censuses across time and space. Thus, digitized versions of individual and family lifespans are established, and individuals and families can be located spatially.
 
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  ISSN ISBN 978-5-7996-2656-3 Medium  
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  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ PFL2019 Serial (down) 3351  
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Author Alicia Fornes; Josep Llados; Joana Maria Pujadas-Mora edit  url
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  Title Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images Type Book Chapter
  Year 2020 Publication Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends Abbreviated Journal  
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  Publisher World Scientific Place of Publication Editor  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-981-120-323-7 Medium  
  Area Expedition Conference  
  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ FLP2020 Serial (down) 3350  
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Author Pau Riba; Josep Llados; Alicia Fornes edit  url
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  Title Hierarchical graphs for coarse-to-fine error tolerant matching Type Journal Article
  Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 134 Issue Pages 116-124  
  Keywords Hierarchical graph representation; Coarse-to-fine graph matching; Graph-based retrieval  
  Abstract During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting).  
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  Notes DAG; 600.097; 601.302; 603.057; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ RLF2020 Serial (down) 3349  
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Author Anjan Dutta; Pau Riba; Josep Llados; Alicia Fornes edit   pdf
url  openurl
  Title Hierarchical Stochastic Graphlet Embedding for Graph-based Pattern Recognition Type Journal Article
  Year 2020 Publication Neural Computing and Applications Abbreviated Journal NEUCOMA  
  Volume 32 Issue Pages 11579–11596  
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  Abstract Despite being very successful within the pattern recognition and machine learning community, graph-based methods are often unusable because of the lack of mathematical operations defined in graph domain. Graph embedding, which maps graphs to a vectorial space, has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques. However, it is well known that graph embedding functions usually suffer from the loss of structural information. In this paper, we consider the hierarchical structure of a graph as a way to mitigate this loss of information. The hierarchical structure is constructed by topologically clustering the graph nodes and considering each cluster as a node in the upper hierarchical level. Once this hierarchical structure is constructed, we consider several configurations to define the mapping into a vector space given a classical graph embedding, in particular, we propose to make use of the stochastic graphlet embedding (SGE). Broadly speaking, SGE produces a distribution of uniformly sampled low-to-high-order graphlets as a way to embed graphs into the vector space. In what follows, the coarse-to-fine structure of a graph hierarchy and the statistics fetched by the SGE complements each other and includes important structural information with varied contexts. Altogether, these two techniques substantially cope with the usual information loss involved in graph embedding techniques, obtaining a more robust graph representation. This fact has been corroborated through a detailed experimental evaluation on various benchmark graph datasets, where we outperform the state-of-the-art methods.  
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  Area Expedition Conference  
  Notes DAG; 600.140; 600.121; 600.141 Approved no  
  Call Number Admin @ si @ DRL2020 Serial (down) 3348  
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