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Author Lluis Gomez; Andres Mafla; Marçal Rusiñol; Dimosthenis Karatzas edit   pdf
url  openurl
  Title Single Shot Scene Text Retrieval Type Conference Article
  Year 2018 Publication 15th European Conference on Computer Vision Abbreviated Journal  
  Volume 11218 Issue Pages 728-744  
  Keywords Image retrieval; Scene text; Word spotting; Convolutional Neural Networks; Region Proposals Networks; PHOC  
  Abstract Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The novelty of the proposed model consists in the usage of a single shot CNN architecture that predicts at the same time bounding boxes and a compact text representation of the words in them. In this way, the text based image retrieval task can be casted as a simple nearest neighbor search of the query text representation over the outputs of the CNN over the entire image
database. Our experiments demonstrate that the proposed architecture
outperforms previous state-of-the-art while it offers a significant increase
in processing speed.
 
  Address (up) Munich; September 2018  
  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 ECCV  
  Notes DAG; 600.084; 601.338; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ GMR2018 Serial 3143  
Permanent link to this record
 

 
Author Gemma Sanchez; Josep Llados; Enric Marti edit   pdf
openurl 
  Title A string-based method to recognize symbols and structural textures in architectural plans Type Conference Article
  Year 1997 Publication 2nd IAPR Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This paper deals with the recognition of symbols and struc- tural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clus- tering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the simila- rity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion.  
  Address (up) Nancy, France  
  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; IAM Approved no  
  Call Number IAM @ iam @ SLE1997 Serial 1498  
Permanent link to this record
 

 
Author Nuria Cirera; Alicia Fornes; Josep Llados edit   pdf
url  doi
openurl 
  Title Hidden Markov model topology optimization for handwriting recognition Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 626-630  
  Keywords  
  Abstract In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task.
 
  Address (up) 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 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.061; 602.006; 600.077 Approved no  
  Call Number Admin @ si @ CFL2015 Serial 2639  
Permanent link to this record
 

 
Author J. Chazalon; Marçal Rusiñol; Jean-Marc Ogier edit  doi
openurl 
  Title Improving Document Matching Performance by Local Descriptor Filtering Type Conference Article
  Year 2015 Publication 6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 Abbreviated Journal  
  Volume Issue Pages 1216 - 1220  
  Keywords  
  Abstract In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework. In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25 000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using
ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements.
 
  Address (up) 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 ISBN Medium  
  Area Expedition Conference CBDAR  
  Notes DAG; 600.077; 601.223; 600.084 Approved no  
  Call Number Admin @ si @ CRO2015a Serial 2680  
Permanent link to this record
 

 
Author Jean-Christophe Burie; J. Chazalon; M. Coustaty; S. Eskenazi; Muhammad Muzzamil Luqman; M. Mehri; Nibal Nayef; Jean-Marc Ogier; S. Prum; Marçal Rusiñol edit  url
doi  openurl
  Title ICDAR2015 Competition on Smartphone Document Capture and OCR (SmartDoc) Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 1161 - 1165  
  Keywords  
  Abstract Smartphones are enabling new ways of capture,
hence arises the need for seamless and reliable acquisition and
digitization of documents, in order to convert them to editable,
searchable and a more human-readable format. Current stateof-the-art
works lack databases and baseline benchmarks for
digitizing mobile captured documents. We have organized a
competition for mobile document capture and OCR in order to
address this issue. The competition is structured into two independent
challenges: smartphone document capture, and smartphone
OCR. This report describes the datasets for both challenges
along with their ground truth, details the performance evaluation
protocols which we used, and presents the final results of the
participating methods. In total, we received 13 submissions: 8
for challenge-I, and 5 for challenge-2.
 
  Address (up) 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 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.077; 601.223; 600.084 Approved no  
  Call Number Admin @ si @ BCC2015 Serial 2681  
Permanent link to this record
 

 
Author Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados edit   pdf
doi  openurl
  Title Towards Query-by-Speech Handwritten Keyword Spotting Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 501-505  
  Keywords  
  Abstract In this paper, we present a new querying paradigm for handwritten keyword spotting. We propose to represent handwritten word images both by visual and audio representations, enabling a query-by-speech keyword spotting system. The two representations are merged together and projected to a common sub-space in the training phase. This transform allows to, given a spoken query, retrieve word instances that were only represented by the visual modality. In addition, the same method can be used backwards at no additional cost to produce a handwritten text-tospeech system. We present our first results on this new querying mechanism using synthetic voices over the George Washington
dataset.
 
  Address (up) 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 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.084; 600.061; 601.223; 600.077;ADAS Approved no  
  Call Number Admin @ si @ RAT2015b Serial 2682  
Permanent link to this record
 

 
Author Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados; R.Jain; D.Doermann edit  url
doi  openurl
  Title Novel Line Verification for Multiple Instance Focused Retrieval in Document Collections Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 481-485  
  Keywords  
  Abstract  
  Address (up) 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 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.077; 601.223; 600.084; 600.061 Approved no  
  Call Number Admin @ si @ GRK2015 Serial 2683  
Permanent link to this record
 

 
Author Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier; Josep Llados edit   pdf
doi  openurl
  Title A Comparative Study of Local Detectors and Descriptors for Mobile Document Classification Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 596-600  
  Keywords  
  Abstract In this paper we conduct a comparative study of local key-point detectors and local descriptors for the specific task of mobile document classification. A classification architecture based on direct matching of local descriptors is used as baseline for the comparative study. A set of four different key-point
detectors and four different local descriptors are tested in all the possible combinations. The experiments are conducted in a database consisting of 30 model documents acquired on 6 different backgrounds, totaling more than 36.000 test images.
 
  Address (up) 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 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.084; 600.61; 601.223; 600.077 Approved no  
  Call Number Admin @ si @ RCO2015 Serial 2684  
Permanent link to this record
 

 
Author J. Chazalon; Marçal Rusiñol; Jean-Marc Ogier; Josep Llados edit  url
doi  openurl
  Title A Semi-Automatic Groundtruthing Tool for Mobile-Captured Document Segmentation Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 621-625  
  Keywords  
  Abstract This paper presents a novel way to generate groundtruth data for the evaluation of mobile document capture systems, focusing on the first stage of the image processing pipeline involved: document object detection and segmentation in lowquality preview frames. We introduce and describe a simple, robust and fast technique based on color markers which enables a semi-automated annotation of page corners. We also detail a technique for marker removal. Methods and tools presented in the paper were successfully used to annotate, in few hours, 24889
frames in 150 video files for the smartDOC competition at ICDAR 2015
 
  Address (up) 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 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.084; 600.061; 601.223; 600.077 Approved no  
  Call Number Admin @ si @ CRO2015b Serial 2685  
Permanent link to this record
 

 
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  
  Keywords  
  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 (up) 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 ISBN Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ NBL2015 Serial 2692  
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