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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 (up) 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  
  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 (up) 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  
  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 (up) 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  
  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 (up) 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  
  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  
Permanent link to this record
 

 
Author Manuel Carbonell; Mauricio Villegas; Alicia Fornes; Josep Llados edit   pdf
openurl 
  Title Joint Recognition of Handwritten Text and Named Entities with a Neural End-to-end Model Type Conference Article
  Year 2018 Publication (up) 13th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 399-404  
  Keywords Named entity recognition; Handwritten Text Recognition; neural networks  
  Abstract When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the
performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different
configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing.
 
  Address Vienna; Austria; April 2018  
  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 DAS  
  Notes DAG; 600.097; 603.057; 601.311; 600.121 Approved no  
  Call Number Admin @ si @ CVF2018 Serial 3170  
Permanent link to this record
 

 
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 (up) 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  
  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 GREC  
  Notes DAG; 600.140; 601.302; 600.121 Approved no  
  Call Number Admin @ si @ BRF2019 Serial 3354  
Permanent link to this record
 

 
Author Miquel Ferrer; Ernest Valveny; F. Serratosa; I. Bardaji; Horst Bunke edit  doi
isbn  openurl
  Title Graph-based k-means clustering: A comparison of the set versus the generalized median graph Type Conference Article
  Year 2009 Publication (up) 13th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 5702 Issue Pages 342–350  
  Keywords  
  Abstract In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph.  
  Address Münster, Germany  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-03766-5 Medium  
  Area Expedition Conference CAIP  
  Notes DAG Approved no  
  Call Number DAG @ dag @ FVS2009d Serial 1219  
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 (up) 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 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 Pau Riba; Josep Llados; Alicia Fornes edit   pdf
url  doi
openurl 
  Title Handwritten Word Spotting by Inexact Matching of Grapheme Graphs Type Conference Article
  Year 2015 Publication (up) 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 781 - 785  
  Keywords  
  Abstract This paper presents a graph-based word spotting for handwritten documents. Contrary to most word spotting techniques, which use statistical representations, we propose a structural representation suitable to be robust to the inherent deformations of handwriting. Attributed graphs are constructed using a part-based approach. Graphemes extracted from shape convexities are used as stable units of handwriting, and are associated to graph nodes. Then, spatial relations between them determine graph edges. Spotting is defined in terms of an error-tolerant graph matching using bipartite-graph matching algorithm. To make the method usable in large datasets, a graph indexing approach that makes use of binary embeddings is used as preprocessing. Historical documents are used as experimental framework. The approach is comparable to statistical ones in terms of time and memory requirements, especially when dealing with large document collections.  
  Address  
  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; 600.061; 602.006 Approved no  
  Call Number Admin @ si @ RLF2015b Serial 2642  
Permanent link to this record
 

 
Author Jean-Christophe Burie; J. Chazalon; M.Coustaty; S.Eskenazi; Muhammad Muzzamil Luqman; M.Mehri; N.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 (up) 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 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  
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