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Author Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez; Josep Llados edit  openurl
  Title Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans Type Conference Article
  Year 2013 Publication 10th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address Bethlehem; PA; USA; August 2013  
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  Area Expedition Conference GREC  
  Notes DAG; 600.045; 600.061; 600.056 Approved no  
  Call Number Admin @ si @ HFF2013b Serial 2695  
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Author Dimosthenis Karatzas; Lluis Gomez; Anguelos Nicolaou; Suman Ghosh; Andrew Bagdanov; Masakazu Iwamura; J. Matas; L. Neumann; V. Ramaseshan; S. Lu ; Faisal Shafait; Seiichi Uchida; Ernest Valveny edit  doi
openurl 
  Title ICDAR 2015 Competition on Robust Reading Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 1156-1160  
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.077; 600.084 Approved no  
  Call Number Admin @ si @ KGN2015 Serial 2690  
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Author Lluis Gomez; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Object Proposals for Text Extraction in the Wild Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 206 - 210  
  Keywords  
  Abstract Object Proposals is a recent computer vision technique receiving increasing interest from the research community. Its main objective is to generate a relatively small set of bounding box proposals that are most likely to contain objects of interest. The use of Object Proposals techniques in the scene text understanding field is innovative. Motivated by the success of powerful while expensive techniques to recognize words in a holistic way, Object Proposals techniques emerge as an alternative to the traditional text detectors. In this paper we study to what extent the existing generic Object Proposals methods may be useful for scene text understanding. Also, we propose a new Object Proposals algorithm that is specifically designed for text and compare it with other generic methods in the state of the art. Experiments show that our proposal is superior in its ability of producing good quality word proposals in an efficient way. The source code of our method is made publicly available  
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  Notes DAG; 600.077; 600.084; 601.197 Approved no  
  Call Number Admin @ si @ GoK2015 Serial 2691  
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Author Anguelos Nicolaou; Andrew Bagdanov; Marcus Liwicki; Dimosthenis Karatzas edit   pdf
url  openurl
  Title Sparse Radial Sampling LBP for Writer Identification Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 716-720  
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  Abstract In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features.  
  Address Nancy; France; August 2015  
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  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ NBL2015 Serial 2692  
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Author Suman Ghosh; Lluis Gomez; Dimosthenis Karatzas; Ernest Valveny edit   pdf
doi  openurl
  Title Efficient indexing for Query By String text retrieval Type Conference Article
  Year 2015 Publication 6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 Abbreviated Journal  
  Volume Issue Pages 1236 - 1240  
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  Abstract This paper deals with Query By String word spotting in scene images. A hierarchical text segmentation algorithm based on text specific selective search is used to find text regions. These regions are indexed per character n-grams present in the text region. An attribute representation based on Pyramidal Histogram of Characters (PHOC) is used to compare text regions with the query text. For generation of the index a similar attribute space based Pyramidal Histogram of character n-grams is used. These attribute models are learned using linear SVMs over the Fisher Vector [1] representation of the images along with the PHOC labels of the corresponding strings.  
  Address Nancy; France; August 2015  
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  Language Summary Language Original Title  
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  Area Expedition Conference CBDAR  
  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ GGK2015 Serial 2693  
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Author J.Kuhn; A.Nussbaumer; J.Pirker; Dimosthenis Karatzas; A. Pagani; O.Conlan; M.Memmel; C.M.Steiner; C.Gutl; D.Albert; Andreas Dengel edit  url
doi  openurl
  Title Advancing Physics Learning Through Traversing a Multi-Modal Experimentation Space Type Conference Article
  Year 2015 Publication Workshop Proceedings on the 11th International Conference on Intelligent Environments Abbreviated Journal  
  Volume 19 Issue Pages 373-380  
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  Abstract Translating conceptual knowledge into real world experiences presents a significant educational challenge. This position paper presents an approach that supports learners in moving seamlessly between conceptual learning and their application in the real world by bringing physical and virtual experiments into everyday settings. Learners are empowered in conducting these situated experiments in a variety of physical settings by leveraging state of the art mobile, augmented reality, and virtual reality technology. A blend of mobile-based multi-sensory physical experiments, augmented reality and enabling virtual environments can allow learners to bridge their conceptual learning with tangible experiences in a completely novel manner. This approach focuses on the learner by applying self-regulated personalised learning techniques, underpinned by innovative pedagogical approaches and adaptation techniques, to ensure that the needs and preferences of each learner are catered for individually.  
  Address Praga; Chzech Republic; July 2015  
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  Area Expedition Conference IE  
  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ KNP2015 Serial 2694  
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Author Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez edit  openurl
  Title Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies Type Conference Article
  Year 2013 Publication 10th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Address Bethlehem; PA; USA; August 2013  
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  Series Volume Series Issue Edition  
  ISSN (down) ISBN Medium  
  Area Expedition Conference GREC  
  Notes DAG Approved no  
  Call Number Admin @ si @ HVS2013b Serial 2696  
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Author Suman Ghosh; Ernest Valveny edit   pdf
url  doi
openurl 
  Title Query by String word spotting based on character bi-gram indexing Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 881-885  
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  Abstract In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC). These attribute models are learned using linear SVMs over the Fisher Vector representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi- gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasets  
  Address Nancy; France; August 2015  
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ GhV2015a Serial 2715  
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Author R. Bertrand; Oriol Ramos Terrades; P. Gomez-Kramer; P. Franco; Jean-Marc Ogier edit  doi
openurl 
  Title A Conditional Random Field model for font forgery detection Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 576 - 580  
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  Abstract Nowadays, document forgery is becoming a real issue. A large amount of documents that contain critical information as payment slips, invoices or contracts, are constantly subject to fraudster manipulation because of the lack of security regarding this kind of document. Previously, a system to detect fraudulent documents based on its intrinsic features has been presented. It was especially designed to retrieve copy-move forgery and imperfection due to fraudster manipulation. However, when a set of characters is not present in the original document, copy-move forgery is not feasible. Hence, the fraudster will use a text toolbox to add or modify information in the document by imitating the font or he will cut and paste characters from another document where the font properties are similar. This often results in font type errors. Thus, a clue to detect document forgery consists of finding characters, words or sentences in a document with font properties different from their surroundings. To this end, we present in this paper an automatic forgery detection method based on document font features. Using the Conditional Random Field a measurement of probability that a character belongs to a specific font is made by comparing the character font features to a knowledge database. Then, the character is classified as a genuine or a fake one by comparing its probability to belong to a certain font type with those of the neighboring characters.  
  Address Nancy; France; August 2015  
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ BRG2015 Serial 2725  
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Author Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados; David Fernandez; Cristina Cañero edit  doi
openurl 
  Title Use case visual Bag-of-Words techniques for camera based identity document classification Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 721 - 725  
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  Abstract Nowadays, automatic identity document recognition, including passport and driving license recognition, is at the core of many applications within the administrative and service sectors, such as police, hospitality, car renting, etc. In former years, the document information was manually extracted whereas today this data is recognized automatically from images obtained by flat-bed scanners. Yet, since these scanners tend to be expensive and voluminous, companies in the sector have recently turned their attention to cheaper, small and yet computationally powerful scanners: the mobile devices. The document identity recognition from mobile images enclose several new difficulties w.r.t traditional scanned images, such as the loss of a controlled background, perspective, blurring, etc. In this paper we present a real application for identity document classification of images taken from mobile devices. This classification process is of extreme importance since a prior knowledge of the document type and origin strongly facilitates the subsequent information extraction. The proposed method is based on a traditional Bagof-Words in which we have taken into consideration several key aspects to enhance recognition rate. The method performance has been studied on three datasets containing more than 2000 images from 129 different document classes.  
  Address Nancy; France; August 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 (down) ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.077; 600.061; Approved no  
  Call Number Admin @ si @ HRL2015a Serial 2726  
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