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Author Alicia Fornes; Josep Llados; Gemma Sanchez; Dimosthenis Karatzas edit  doi
openurl 
  Title Rotation Invariant Hand-Drawn Symbol Recognition based on a Dynamic Time Warping Model Type Journal Article
  Year 2010 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 13 Issue 3 Pages 229–241  
  Keywords  
  Abstract One of the major difficulties of handwriting symbol recognition is the high variability among symbols because of the different writer styles. In this paper, we introduce a robust approach for describing and recognizing hand-drawn symbols tolerant to these writer style differences. This method, which is invariant to scale and rotation, is based on the dynamic time warping (DTW) algorithm. The symbols are described by vector sequences, a variation of the DTW distance is used for computing the matching distance, and K-Nearest Neighbor is used to classify them. Our approach has been evaluated in two benchmarking scenarios consisting of hand-drawn symbols. Compared with state-of-the-art methods for symbol recognition, our method shows higher tolerance to the irregular deformations induced by hand-drawn strokes.  
  Address  
  Corporate Author Thesis  
  Publisher Springer-Verlag Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1433-2833 ISBN Medium (up)  
  Area Expedition Conference  
  Notes DAG; IF 2009: 1,213 Approved no  
  Call Number DAG @ dag @ FLS2010a Serial 1288  
Permanent link to this record
 

 
Author Mathieu Nicolas Delalandre; Ernest Valveny; Tony Pridmore; Dimosthenis Karatzas edit  doi
openurl 
  Title Generation of Synthetic Documents for Performance Evaluation of Symbol Recognition & Spotting Systems Type Journal Article
  Year 2010 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 13 Issue 3 Pages 187-207  
  Keywords  
  Abstract This paper deals with the topic of performance evaluation of symbol recognition & spotting systems. We propose here a new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context. This approach is based on the definition of a set of constraints that permit us to place the symbols on a pre-defined background according to the properties of a particular domain (architecture, electronics, engineering, etc.). In this way, we can obtain a large amount of images resembling real documents by simply defining the set of constraints and providing a few pre-defined backgrounds. As documents are synthetically generated, the groundtruth (the location and the label of every symbol) becomes automatically available. We have applied this approach to the generation of a large database of architectural drawings and electronic diagrams, which shows the flexibility of the system. Performance evaluation experiments of a symbol localization system show that our approach permits to generate documents with different features that are reflected in variation of localization results.  
  Address  
  Corporate Author Thesis  
  Publisher Springer-Verlag Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1433-2833 ISBN Medium (up)  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number DAG @ dag @ DVP2010 Serial 1289  
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Author Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados edit  url
doi  openurl
  Title Unsupervised writer adaptation of whole-word HMMs with application to word-spotting Type Journal Article
  Year 2010 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 31 Issue 8 Pages 742–749  
  Keywords Word-spotting; Handwriting recognition; Writer adaptation; Hidden Markov model; Document analysis  
  Abstract In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters.

Our main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best of our knowledge, this is the first work dealing with adaptation for word-spotting. The preliminary version of this paper obtained an IBM Best Student Paper Award at the 19th International Conference on Pattern Recognition.
 
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium (up)  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number DAG @ dag @ RPS2010 Serial 1290  
Permanent link to this record
 

 
Author Miquel Ferrer; Ernest Valveny; F. Serratosa; K. Riesen; Horst Bunke edit  url
doi  openurl
  Title Generalized Median Graph Computation by Means of Graph Embedding in Vector Spaces Type Journal Article
  Year 2010 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 43 Issue 4 Pages 1642–1655  
  Keywords Graph matching; Weighted mean of graphs; Median graph; Graph embedding; Vector spaces  
  Abstract The median graph has been presented as a useful tool to represent a set of graphs. Nevertheless its computation is very complex and the existing algorithms are restricted to use limited amount of data. In this paper we propose a new approach for the computation of the median graph based on graph embedding. Graphs are embedded into a vector space and the median is computed in the vector domain. We have designed a procedure based on the weighted mean of a pair of graphs to go from the vector domain back to the graph domain in order to obtain a final approximation of the median graph. Experiments on three different databases containing large graphs show that we succeed to compute good approximations of the median graph. We have also applied the median graph to perform some basic classification tasks achieving reasonable good results. These experiments on real data open the door to the application of the median graph to a number of more complex machine learning algorithms where a representative of a set of graphs is needed.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium (up)  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number DAG @ dag @ FVS2010 Serial 1294  
Permanent link to this record
 

 
Author Alicia Fornes; Josep Llados; Gemma Sanchez; Xavier Otazu; Horst Bunke edit  doi
openurl 
  Title A Combination of Features for Symbol-Independent Writer Identification in Old Music Scores Type Journal Article
  Year 2010 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 13 Issue 4 Pages 243-259  
  Keywords  
  Abstract The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper, we present an architecture for writer identification in old handwritten music scores. Even though an important amount of music compositions contain handwritten text, the aim of our work is to use only music notation to determine the author. The main contribution is therefore the use of features extracted from graphical alphabets. Our proposal consists in combining the identification results of two different approaches, based on line and textural features. The steps of the ensemble architecture are the following. First of all, the music sheet is preprocessed for removing the staff lines. Then, music lines and texture images are generated for computing line features and textural features. Finally, the classification results are combined for identifying the writer. The proposed method has been tested on a database of old music scores from the seventeenth to nineteenth centuries, achieving a recognition rate of about 92% with 20 writers.  
  Address  
  Corporate Author Thesis  
  Publisher Springer-Verlag Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1433-2833 ISBN Medium (up)  
  Area Expedition Conference  
  Notes DAG; CAT;CIC Approved no  
  Call Number FLS2010b Serial 1319  
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