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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Efficient Logo Retrieval Through Hashing Shape Context Descriptors |
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Conference Article |
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2010 |
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9th IAPR International Workshop on Document Analysis Systems |
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215–222 |
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In this paper, we present an approach towards the retrieval of words from graphical document images. In graphical documents, due to presence of multi-oriented characters in non-structured layout, word indexing is a challenging task. The proposed approach uses recognition results of individual components to form character pairs with the neighboring components. An indexing scheme is designed to store the spatial description of components and to access them efficiently. Given a query text word (ascii/unicode format), the character pairs present in it are searched in the document. Next the retrieved character pairs are linked sequentially to form character string. Dynamic programming is applied to find different instances of query words. A string edit distance is used here to match the query word as the objective function. Recognition of multi-scale and multi-oriented character component is done using Support Vector Machine classifier. To consider multi-oriented character strings the features used in the SVM are invariant to character orientation. Experimental results show that the method is efficient to locate a query word from multi-oriented text in graphical documents. |
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Boston; USA |
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DAG @ dag @ RuL2010b |
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1434 |
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Author |
Farshad Nourbakhsh; Dimosthenis Karatzas; Ernest Valveny |
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Title |
A polar-based logo representation based on topological and colour features |
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Conference Article |
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2010 |
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9th IAPR International Workshop on Document Analysis Systems |
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341–348 |
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In this paper, we propose a novel rotation and scale invariant method for colour logo retrieval and classification, which involves performing a simple colour segmentation and subsequently describing each of the resultant colour components based on a set of topological and colour features. A polar representation is used to represent the logo and the subsequent logo matching is based on Cyclic Dynamic Time Warping (CDTW). We also show how combining information about the global distribution of the logo components and their local neighbourhood using the Delaunay triangulation allows to improve the results. All experiments are performed on a dataset of 2500 instances of 100 colour logo images in different rotations and scales. |
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Boston; USA; |
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978-1-60558-773-8 |
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DAG @ dag @ NKV2010 |
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1436 |
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Author |
Sebastien Mace; Herve Locteau; Ernest Valveny; Salvatore Tabbone |
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Title |
A system to detect rooms in architectural floor plan images |
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Conference Article |
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2010 |
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9th IAPR International Workshop on Document Analysis Systems |
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167–174 |
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In this article, a system to detect rooms in architectural floor plan images is described. We first present a primitive extraction algorithm for line detection. It is based on an original coupling of classical Hough transform with image vectorization in order to perform robust and efficient line detection. We show how the lines that satisfy some graphical arrangements are combined into walls. We also present the way we detect some door hypothesis thanks to the extraction of arcs. Walls and door hypothesis are then used by our room segmentation strategy; it consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines between regions can also be rough. We take advantage of knowledge associated to architectural floor plans in order to obtain mostly rectangular rooms. Qualitative and quantitative evaluations performed on a corpus of real documents show promising results. |
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Boston; USA |
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978-1-60558-773-8 |
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DAG @ dag @ MLV2010 |
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1437 |
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Author |
T.O. Nguyen; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Symbol Descriptor Based on Shape Context and Vector Model of Information Retrieval |
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Conference Article |
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2008 |
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Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, |
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191-197 |
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Nara, Japan |
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Admin @ si @ NTR2008a |
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1873 |
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Author |
Albert Gordo; Florent Perronnin; Ernest Valveny |
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Title |
Document classification using multiple views |
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2012 |
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10th IAPR International Workshop on Document Analysis Systems |
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33-37 |
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The combination of multiple features or views when representing documents or other kinds of objects usually leads to improved results in classification (and retrieval) tasks. Most systems assume that those views will be available both at training and test time. However, some views may be too `expensive' to be available at test time. In this paper, we consider the use of Canonical Correlation Analysis to leverage `expensive' views that are available only at training time. Experimental results show that this information may significantly improve the results in a classification task. |
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Australia |
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IEEE Computer Society Washington |
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978-0-7695-4661-2 |
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Admin @ si @ GPV2012 |
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2049 |
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Author |
David Fernandez; R.Manmatha; Josep Llados; Alicia Fornes |
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Title |
Sequential Word Spotting in Historical Handwritten Documents |
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Conference Article |
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2014 |
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11th IAPR International Workshop on Document Analysis and Systems |
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101 - 105 |
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In this work we present a handwritten word spotting approach that takes advantage of the a priori known order of appearance of the query words. Given an ordered sequence of query word instances, the proposed approach performs a
sequence alignment with the words in the target collection. Although the alignment is quite sparse, i.e. the number of words in the database is higher than the query set, the improvement in the overall performance is sensitively higher than isolated word spotting. As application dataset, we use a collection of handwritten marriage licenses taking advantage of the ordered
index pages of family names. |
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Tours; Francia; April 2014 |
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978-1-4799-3243-6 |
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DAG; 600.061; 600.056; 602.006; 600.077 |
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Admin @ si @ FML2014 |
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2462 |
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Author |
Christophe Rigaud; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier |
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Title |
Color descriptor for content-based drawing retrieval |
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Conference Article |
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2014 |
Publication |
11th IAPR International Workshop on Document Analysis and Systems |
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267 - 271 |
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Human detection in computer vision field is an active field of research. Extending this to human-like drawings such as the main characters in comic book stories is not trivial. Comics analysis is a very recent field of research at the intersection of graphics, texts, objects and people recognition. The detection of the main comic characters is an essential step towards a fully automatic comic book understanding. This paper presents a color-based approach for comics character retrieval using content-based drawing retrieval and color palette. |
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Tours; Francia; April 2014 |
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978-1-4799-3243-6 |
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DAG; 600.056; 600.077 |
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Admin @ si @ RKB2014 |
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2479 |
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Author |
Dimosthenis Karatzas; Sergi Robles; Lluis Gomez |
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Title |
An on-line platform for ground truthing and performance evaluation of text extraction systems |
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Conference Article |
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2014 |
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11th IAPR International Workshop on Document Analysis and Systems |
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242 - 246 |
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This paper presents a set of on-line software tools for creating ground truth and calculating performance evaluation metrics for text extraction tasks such as localization, segmentation and recognition. The platform supports the definition of comprehensive ground truth information at different text representation levels while it offers centralised management and quality control of the ground truthing effort. It implements a range of state of the art performance evaluation algorithms and offers functionality for the definition of evaluation scenarios, on-line calculation of various performance metrics and visualisation of the results. The
presented platform, which comprises the backbone of the ICDAR 2011 (challenge 1) and 2013 (challenges 1 and 2) Robust Reading competitions, is now made available for public use. |
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Tours; Francia; April 2014 |
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978-1-4799-3243-6 |
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DAG; 600.056; 600.077 |
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Admin @ si @ KRG2014 |
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2491 |
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Author |
P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes |
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Title |
A Novel Learning-free Word Spotting Approach Based on Graph Representation |
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Conference Article |
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2014 |
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11th IAPR International Workshop on Document Analysis and Systems |
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207-211 |
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Effective information retrieval on handwritten document images has always been a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment result is introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods. |
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Tours; France; April 2014 |
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978-1-4799-3243-6 |
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DAG; 600.061; 602.006; 600.077 |
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Admin @ si @ WEG2014b |
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2517 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Spotting Symbol Using Sparsity over Learned Dictionary of Local Descriptors |
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Conference Article |
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Year |
2014 |
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11th IAPR International Workshop on Document Analysis and Systems |
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156-160 |
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This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising. |
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978-1-4799-3243-6 |
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DAG; 600.077 |
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Admin @ si @ DTR2014 |
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2543 |
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