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Author |
David Fernandez; Josep Llados; Alicia Fornes; R.Manmatha |


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Title  |
On Influence of Line Segmentation in Efficient Word Segmentation in Old Manuscripts |
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Conference Article |
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2012 |
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13th International Conference on Frontiers in Handwriting Recognition |
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763-768 |
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document image processing;handwritten character recognition;history;image segmentation;Spanish document;historical document;line segmentation;old handwritten document;old manuscript;word segmentation;Bifurcation;Dynamic programming;Handwriting recognition;Image segmentation;Measurement;Noise;Skeleton;Segmentation;document analysis;document and text processing;handwriting analysis;heuristics;path-finding |
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Abstract |
he objective of this work is to show the importance of a good line segmentation to obtain better results in the segmentation of words of historical documents. We have used the approach developed by Manmatha and Rothfeder [1] to segment words in old handwritten documents. In their work the lines of the documents are extracted using projections. In this work, we have developed an approach to segment lines more efficiently. The new line segmentation algorithm tackles with skewed, touching and noisy lines, so it is significantly improves word segmentation. Experiments using Spanish documents from the Marriages Database of the Barcelona Cathedral show that this approach reduces the error rate by more than 20% |
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978-1-4673-2262-1 |
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ICFHR |
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Admin @ si @ FLF2012 |
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2200 |
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Author |
Alicia Fornes; Josep Llados; Gemma Sanchez |

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Title  |
Old Handwritten Musical Symbol Classification by a Dynamic TimeWrapping Based Method |
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2008 |
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Graphics Recognition: Recent Advances and New Opportunities |
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5046 |
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52–60 |
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W. Liu, J. Llados, J.M. Ogier |
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DAG @ dag @ FLS2008a |
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990 |
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Author |
Alicia Fornes; Josep Llados; Gemma Sanchez |

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Title  |
Old Handwritten Musical Symbol Classification by a Dynamic Time Warping Based Method |
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2007 |
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Seventh IAPR International Workshop on Graphics Recognition |
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26–27 |
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Curitiba (Brazil) |
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GREC |
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DAG |
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DAG @ dag @ FLS2007 |
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887 |
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Author |
Ali Furkan Biten; Ruben Tito; Lluis Gomez; Ernest Valveny; Dimosthenis Karatzas |


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Title  |
OCR-IDL: OCR Annotations for Industry Document Library Dataset |
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2022 |
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ECCV Workshop on Text in Everything |
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Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of pretraining data with different OCR engines leading to incomparable results between models. In other words, it is not obvious whether the performance gain is coming from diverse usage of amount of data and distinct OCR engines or from the proposed models. To remedy the problem, we make public the OCR annotations for IDL documents using commercial OCR engine given their superior performance over open source OCR models. The contributed dataset (OCR-IDL) has an estimated monetary value over 20K US$. It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence. All of our data and its collection process with the annotations can be found in this https URL. |
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ECCV |
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DAG; no proj |
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Admin @ si @ BTG2022 |
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3817 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |


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Title  |
Object Proposals for Text Extraction in the Wild |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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206 - 210 |
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Object Proposals is a recent computer vision technique receiving increasing interest from the research community. Its main objective is to generate a relatively small set of bounding box proposals that are most likely to contain objects of interest. The use of Object Proposals techniques in the scene text understanding field is innovative. Motivated by the success of powerful while expensive techniques to recognize words in a holistic way, Object Proposals techniques emerge as an alternative to the traditional text detectors. In this paper we study to what extent the existing generic Object Proposals methods may be useful for scene text understanding. Also, we propose a new Object Proposals algorithm that is specifically designed for text and compare it with other generic methods in the state of the art. Experiments show that our proposal is superior in its ability of producing good quality word proposals in an efficient way. The source code of our method is made publicly available |
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ICDAR |
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DAG; 600.077; 600.084; 601.197 |
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Admin @ si @ GoK2015 |
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2691 |
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Author |
Agnes Borras; Josep Llados |


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Title  |
Object Image Retrieval by Shape Content in Complex Scenes Using Geometric Constraints |
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Book Chapter |
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Year |
2005 |
Publication |
Pattern Recognition And Image Analysis |
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3522 |
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325–332 |
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This paper presents an image retrieval system based on 2D shape information. Query shape objects and database images are repre- sented by polygonal approximations of their contours. Afterwards they are encoded, using geometric features, in terms of predefined structures. Shapes are then located in database images by a voting procedure on the spatial domain. Then an alignment matching provides a probability value to rank de database image in the retrieval result. The method al- lows to detect a query object in database images even when they contain complex scenes. Also the shape matching tolerates partial occlusions and affine transformations as translation, rotation or scaling. |
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Estoril (Portugal) |
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Springer Link |
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DAG @ dag @ BoL2005; IAM @ iam @ BoL2005 |
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556 |
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Author |
Ernest Valveny; Antonio Lopez |

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Title  |
Numeral Recognition for Quality Control of Surgical Sachets |
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Miscellaneous |
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2003 |
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Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR´03), 379–383 |
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DAG;ADAS |
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ADAS @ adas @ VaL2003 |
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423 |
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Author |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados; R.Jain; D.Doermann |


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Title  |
Novel Line Verification for Multiple Instance Focused Retrieval in Document Collections |
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Conference Article |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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481-485 |
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Nancy; France; August 2015 |
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ICDAR |
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DAG; 600.077; 601.223; 600.084; 600.061 |
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Admin @ si @ GRK2015 |
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2683 |
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Author |
Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny |


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Title  |
Notation-invariant patch-based wall detector in architectural floor plans |
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Book Chapter |
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2013 |
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Graphics Recognition. New Trends and Challenges |
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7423 |
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79--88 |
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Architectural floor plans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed in [1], showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. This paper presents an evolution of that previous work, after analyzing and testing several alternatives for each of the different steps of the method: Firstly, an automatic plan-size normalization process is done. Secondly we evaluate different features to obtain the description of every patch. Thirdly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These variations of the method have been tested for wall detection on two datasets of architectural floor plans with different notations. After studying in deep each of the steps in the process pipeline, we are able to find the best system configuration, which highly outperforms the results on wall segmentation obtained by the original paper. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-36823-3 |
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DAG; 600.045; 600.056; 605.203 |
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Admin @ si @ HMS2013 |
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2322 |
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Author |
Robert Benavente; Gemma Sanchez; Ramon Baldrich; Maria Vanrell; Josep Llados |

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Title  |
Normalized colour segmentation for human appearance description. |
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2000 |
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15 th International Conference on Pattern Recognition |
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3 |
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637-641 |
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Barcelona. |
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ICPR |
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DAG;CIC |
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CAT @ cat @ BSB2000 |
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223 |
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