toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
Details
  Records Links
Author Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes edit   pdf
doi  openurl
  Title Optical Music Recognition by Recurrent Neural Networks Type Conference Article
  Year (down) 2017 Publication 14th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages 25-26  
  Keywords Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory  
  Abstract Optical Music Recognition is the task of transcribing a music score into a machine readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level  
  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; 600.097; 601.302; 600.121 Approved no  
  Call Number Admin @ si @ BRC2017 Serial 3056  
Permanent link to this record
 

 
Author ChunYang; Xu Cheng Yin; Hong Yu; Dimosthenis Karatzas; Yu Cao edit  doi
isbn  openurl
  Title ICDAR2017 Robust Reading Challenge on Text Extraction from Biomedical Literature Figures (DeTEXT) Type Conference Article
  Year (down) 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 1444-1447  
  Keywords  
  Abstract Hundreds of millions of figures are available in the biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information and understanding biomedical documents. Unlike images in the open domain, biomedical figures present a variety of unique challenges. For example, biomedical figures typically have complex layouts, small font sizes, short text, specific text, complex symbols and irregular text arrangements. This paper presents the final results of the ICDAR 2017 Competition on Text Extraction from Biomedical Literature Figures (ICDAR2017 DeTEXT Competition), which aims at extracting (detecting and recognizing) text from biomedical literature figures. Similar to text extraction from scene images and web pictures, ICDAR2017 DeTEXT Competition includes three major tasks, i.e., text detection, cropped word recognition and end-to-end text recognition. Here, we describe in detail the data set, tasks, evaluation protocols and participants of this competition, and report the performance of the participating methods.  
  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 978-1-5386-3586-5 Medium  
  Area Expedition Conference ICDAR  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ YCY2017 Serial 3098  
Permanent link to this record
 

 
Author David Aldavert; Marçal Rusiñol; Ricardo Toledo edit   pdf
doi  openurl
  Title Automatic Static/Variable Content Separation in Administrative Document Images Type Conference Article
  Year (down) 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract In this paper we present an automatic method for separating static and variable content from administrative document images. An alignment approach is able to unsupervisedly build probabilistic templates from a set of examples of the same document kind. Such templates define which is the likelihood of every pixel of being either static or variable content. In the extraction step, the same alignment technique is used to match
an incoming image with the template and to locate the positions where variable fields appear. We validate our approach on the public NIST Structured Tax Forms Dataset.
 
  Address Kyoto; Japan; November 2017  
  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; 600.121 Approved no  
  Call Number Admin @ si @ ART2017 Serial 3001  
Permanent link to this record
 

 
Author Dimosthenis Karatzas; Lluis Gomez; Marçal Rusiñol edit   pdf
openurl 
  Title The Robust Reading Competition Annotation and Evaluation Platform Type Conference Article
  Year (down) 2017 Publication 1st International Workshop on Open Services and Tools for Document Analysis Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The ICDAR Robust Reading Competition (RRC), initiated in 2003 and re-established 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 Kyoto; Japan; November 2017  
  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-OST  
  Notes DAG; 600.084; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ KGR2017 Serial 3063  
Permanent link to this record
 

 
Author E. Royer; J. Chazalon; Marçal Rusiñol; F. Bouchara edit   pdf
doi  openurl
  Title Benchmarking Keypoint Filtering Approaches for Document Image Matching Type Conference Article
  Year (down) 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Best Poster Award.
Reducing the amount of keypoints used to index an image is particularly interesting to control processing time and memory usage in real-time document image matching applications, like augmented documents or smartphone applications. This paper benchmarks two keypoint selection methods on a task consisting of reducing keypoint sets extracted from document images, while preserving detection and segmentation accuracy. We first study the different forms of keypoint filtering, and we introduce the use of the CORE selection method on
keypoints extracted from document images. Then, we extend a previously published benchmark by including evaluations of the new method, by adding the SURF-BRISK detection/description scheme, and by reporting processing speeds. Evaluations are conducted on the publicly available dataset of ICDAR2015 SmartDOC challenge 1. Finally, we prove that reducing the original keypoint set is always feasible and can be beneficial
not only to processing speed but also to accuracy.
 
  Address Kyoto; Japan; November 2017  
  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; 600.121 Approved no  
  Call Number Admin @ si @ RCR2017 Serial 3000  
Permanent link to this record
 

 
Author Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel edit  doi
openurl 
  Title Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition Type Book Chapter
  Year (down) 2017 Publication Graphics Recognition. Current Trends and Challenges Abbreviated Journal  
  Volume 9657 Issue Pages  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor B. Lamiroy; R Dueire Lins  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference GREC  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ JLR2017 Serial 2928  
Permanent link to this record
 

 
Author Hana Jarraya; Oriol Ramos Terrades; Josep Llados edit   pdf
url  openurl
  Title Graph Embedding through Probabilistic Graphical Model applied to Symbolic Graphs Type Conference Article
  Year (down) 2017 Publication 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume Issue Pages  
  Keywords Attributed Graph; Probabilistic Graphical Model; Graph Embedding; Structured Support Vector Machines  
  Abstract We propose a new Graph Embedding (GEM) method that takes advantages of structural pattern representation. It models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector. This vector is a signature of AG in a lower dimensional vectorial space. We apply Structured Support Vector Machines (SSVM) to process classification task. As first tentative, results on the GREC dataset are encouraging enough to go further on this direction.  
  Address Faro; Portugal; June 2017  
  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 IbPRIA  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ JRL2017a Serial 2953  
Permanent link to this record
 

 
Author Hana Jarraya; Oriol Ramos Terrades; Josep Llados edit  doi
openurl 
  Title Learning structural loss parameters on graph embedding applied on symbolic graphs Type Conference Article
  Year (down) 2017 Publication 12th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract We propose an amelioration of proposed Graph Embedding (GEM) method in previous work that takes advantages of structural pattern representation and the structured distortion. it models an Attributed Graph (AG) as a Probabilistic Graphical Model (PGM). Then, it learns the parameters of this PGM presented by a vector, as new signature of AG in a lower dimensional vectorial space. We focus to adapt the structured learning algorithm via 1_slack formulation with a suitable risk function, called Graph Edit Distance (GED). It defines the dissimilarity of the ground truth and predicted graph labels. It determines by the error tolerant graph matching using bipartite graph matching algorithm. We apply Structured Support Vector Machines (SSVM) to process classification task. During our experiments, we got our results on the GREC dataset.  
  Address Kyoto; Japan; November 2017  
  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; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ JRL2017b Serial 3073  
Permanent link to this record
 

 
Author J. Chazalon; P. Gomez-Kramer; Jean-Christophe Burie; M.Coustaty; S.Eskenazi; Muhammad Muzzamil Luqman; N.Nayef; Marçal Rusiñol; N. Sidere; Jean-Marc Ogier edit   pdf
doi  openurl
  Title SmartDoc 2017 Video Capture: Mobile Document Acquisition in Video Mode Type Conference Article
  Year (down) 2017 Publication 1st International Workshop on Open Services and Tools for Document Analysis Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract As mobile document acquisition using smartphones is getting more and more common, along with the continuous improvement of mobile devices (both in terms of computing power and image quality), we can wonder to which extent mobile phones can replace desktop scanners. Modern applications can cope with perspective distortion and normalize the contrast of a document page captured with a smartphone, and in some cases like bottle labels or posters, smartphones even have the advantage of allowing the acquisition of non-flat or large documents. However, several cases remain hard to handle, such as reflective documents (identity cards, badges, glossy magazine cover, etc.) or large documents for which some regions require an important amount of detail. This paper introduces the SmartDoc 2017 benchmark (named “SmartDoc Video Capture”), which aims at
assessing whether capturing documents using the video mode of a smartphone could solve those issues. The task under evaluation is both a stitching and a reconstruction problem, as the user can move the device over different parts of the document to capture details or try to erase highlights. The material released consists of a dataset, an evaluation method and the associated tool, a sample method, and the tools required to extend the dataset. All the components are released publicly under very permissive licenses, and we particularly cared about maximizing the ease of
understanding, usage and improvement.
 
  Address Kyoto; Japan; November 2017  
  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-OST  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ CGB2017 Serial 2997  
Permanent link to this record
 

 
Author Juan Ignacio Toledo; Sounak Dey; Alicia Fornes; Josep Llados edit   pdf
openurl 
  Title Handwriting Recognition by Attribute embedding and Recurrent Neural Networks Type Conference Article
  Year (down) 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 1038-1043  
  Keywords  
  Abstract Handwriting recognition consists in obtaining the transcription of a text image. Recent word spotting methods based on attribute embedding have shown good performance when recognizing words. However, they are holistic methods in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently,
these kinds of approaches are not able to deal with out of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address these issues, in this paper we propose a handwriting recognition method that adapts the attribute embedding to sequence learning. Concretely, the method learns the attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain promising results even without the use of any kind of dictionary or language model
 
  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; 600.097; 601.225; 600.121 Approved no  
  Call Number Admin @ si @ TDF2017 Serial 3055  
Permanent link to this record
Select All    Deselect All
 |   | 
Details

Save Citations:
Export Records: