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Author |
Mathieu Nicolas Delalandre; Ernest Valveny; Josep Llados |

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Title  |
Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview |
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Report |
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2008 |
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CVC Technical Report #117 |
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Bellaterra (Spain) |
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DAG |
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no |
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Call Number |
DAG @ dag @ DVL2008a |
Serial |
946 |
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Author |
Mathieu Nicolas Delalandre; Ernest Valveny; Josep Llados |

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Title  |
Performance Evaluation of Symbol Recognition and Spotting Systems |
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Conference Article |
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2008 |
Publication |
Proceedings of the 8th International Workshop on Document Analysis Systems, |
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497–505 |
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Nara (Japan) |
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DAS |
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DAG |
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no |
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Call Number |
DAG @ dag @ DVL2008b |
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1060 |
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Author |
Ernest Valveny; Philippe Dosch |

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Title  |
Performance Evaluation of Symbol Recognition |
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Book Chapter |
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Year |
2004 |
Publication |
Document Analysis Systems |
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LNCS |
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Volume |
3163 |
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Pages |
354–365 |
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Springer-Verlag |
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S. Marinai, A. Dengel (Eds.), |
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3-540-23060-2 |
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DAG |
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DAG @ dag @ VaD2004a |
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502 |
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Author |
Ernest Valveny; Salvatore Tabbone; Oriol Ramos Terrades; Emilie Jean-Marie Odile |

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Title  |
Performance Characterization of Shape Descriptors for Symbol Representation |
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Conference Article |
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Year |
2007 |
Publication |
Seventh IAPR International Workshop on Graphics Recognition |
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82–83 |
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Curitiba (Brazil) |
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GREC |
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DAG |
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no |
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Call Number |
DAG @ dag @ VTR2007 |
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889 |
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Author |
Ernest Valveny; Salvatore Tabbone; Oriol Ramos Terrades |

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Title  |
Performance Characterization of Shape Descriptors for Symbol Representation |
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Book Chapter |
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Year |
2008 |
Publication |
Graphics Recognition: Recent Advances and New Opportunities |
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5046 |
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278–287 |
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W. Liu, J. Llados, J.M. Ogier |
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DAG |
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no |
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DAG @ dag @ VTR2008 |
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985 |
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Author |
Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez;Josep Llados |

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Title  |
Perceptual retrieval of architectural floor plans |
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Conference Article |
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Year |
2013 |
Publication |
10th IAPR International Workshop on Graphics Recognition |
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This paper proposes a runlength histogram signature as a percetual descriptor of architectural plans in a retrieval scenario. The style of an architectural drawing is characterized by the perception of lines, shapes and texture. Such visual stimuli are the basis for defining semantic concepts as space properties, symmetry, density, etc. We propose runlength histograms extracted in vertical, horizontal and diagonal directions as a characterization of line and space properties in floorplans, so it can be roughly associated to a description of walls and room structure. A retrieval application illustrates the performance of the proposed approach, where given a plan as a query,
similar ones are obtained from a database. A ground truth based on human observation has been constructed to validate the hypothesis. Preliminary results show the interest of the proposed approach and opens a challenging research line in graphics recognition. |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG; 600.045; 600.056; 600.061 |
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Admin @ si @ HFF2013a |
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2320 |
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Author |
Lluis Gomez |

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Title  |
Perceptual Organization for Text Extraction in Natural Scenes |
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Report |
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Year |
2012 |
Publication |
CVC Technical Report |
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Volume |
173 |
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Address |
Bellaterra |
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Master's thesis |
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DAG |
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no |
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Admin @ si @ Gom2012 |
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2309 |
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Author |
Marçal Rusiñol; Farshad Nourbakhsh; Dimosthenis Karatzas; Ernest Valveny; Josep Llados |


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Title  |
Perceptual Image Retrieval by Adding Color Information to the Shape Context Descriptor |
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Conference Article |
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Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
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1594–1597 |
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In this paper we present a method for the retrieval of images in terms of perceptual similarity. Local color information is added to the shape context descriptor in order to obtain an object description integrating both shape and color as visual cues. We use a color naming algorithm in order to represent the color information from a perceptual point of view. The proposed method has been tested in two different applications, an object retrieval scenario based on color sketch queries and a color trademark retrieval problem. Experimental results show that the addition of the color information significantly outperforms the sole use of the shape context descriptor. |
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Istanbul (Turkey) |
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1051-4651 |
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978-1-4244-7542-1 |
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ICPR |
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DAG |
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no |
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DAG @ dag @ RNK2010 |
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1435 |
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Author |
Ramon Baldrich; Ricardo Toledo; Ernest Valveny; Maria Vanrell |

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Title  |
Perceptual Colour Image Segmentation. |
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Miscellaneous |
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Year |
2002 |
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Proceeding of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002: 145–150. |
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DAG;CIC;ADAS |
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CAT @ cat @ BTV2002 |
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290 |
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Author |
Lei Kang; Pau Riba; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |


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Title  |
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition |
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Journal Article |
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Year |
2022 |
Publication |
Pattern Recognition |
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PR |
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129 |
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108766 |
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The advent of recurrent neural networks for handwriting recognition marked an important milestone reaching impressive recognition accuracies despite the great variability that we observe across different writing styles. Sequential architectures are a perfect fit to model text lines, not only because of the inherent temporal aspect of text, but also to learn probability distributions over sequences of characters and words. However, using such recurrent paradigms comes at a cost at training stage, since their sequential pipelines prevent parallelization. In this work, we introduce a non-recurrent approach to recognize handwritten text by the use of transformer models. We propose a novel method that bypasses any recurrence. By using multi-head self-attention layers both at the visual and textual stages, we are able to tackle character recognition as well as to learn language-related dependencies of the character sequences to be decoded. Our model is unconstrained to any predefined vocabulary, being able to recognize out-of-vocabulary words, i.e. words that do not appear in the training vocabulary. We significantly advance over prior art and demonstrate that satisfactory recognition accuracies are yielded even in few-shot learning scenarios. |
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Sept. 2022 |
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DAG; 600.121; 600.162 |
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Admin @ si @ KRR2022 |
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3556 |
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