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Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard |
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Title  |
Subgraph Spotting Through Explicit Graph Embedding: An Application to Content Spotting in Graphic Document Images |
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Conference Article |
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2011 |
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11th International Conference on Document Analysis and Recognition |
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870-874 |
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We present a method for spotting a subgraph in a graph repository. Subgraph spotting is a very interesting research problem for various application domains where the use of a relational data structure is mandatory. Our proposed method accomplishes subgraph spotting through graph embedding. We achieve automatic indexation of a graph repository during off-line learning phase, where we (i) break the graphs into 2-node sub graphs (a.k.a. cliques of order 2), which are primitive building-blocks of a graph, (ii) embed the 2-node sub graphs into feature vectors by employing our recently proposed explicit graph embedding technique, (iii) cluster the feature vectors in classes by employing a classic agglomerative clustering technique, (iv) build an index for the graph repository and (v) learn a Bayesian network classifier. The subgraph spotting is achieved during the on-line querying phase, where we (i) break the query graph into 2-node sub graphs, (ii) embed them into feature vectors, (iii) employ the Bayesian network classifier for classifying the query 2-node sub graphs and (iv) retrieve the respective graphs by looking-up in the index of the graph repository. The graphs containing all query 2-node sub graphs form the set of result graphs for the query. Finally, we employ the adjacency matrix of each result graph along with a score function, for spotting the query graph in it. The proposed subgraph spotting method is equally applicable to a wide range of domains, offering ease of query by example (QBE) and granularity of focused retrieval. Experimental results are presented for graphs generated from two repositories of electronic and architectural document images. |
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Beijing, China |
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1520-5363 |
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978-1-4577-1350-7 |
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Admin @ si @ LRL2011 |
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1790 |
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Lluis Gomez; Marçal Rusiñol; Ali Furkan Biten; Dimosthenis Karatzas |
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Subtitulació automàtica d'imatges. Estat de l'art i limitacions en el context arxivístic |
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2018 |
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Jornades Imatge i Recerca |
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DAG; 600.084; 600.135; 601.338; 600.121; 600.129 |
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Admin @ si @ GRB2018 |
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3173 |
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S. Chanda; Oriol Ramos Terrades; Umapada Pal |
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Title |
SVM Based Scheme for Thai and English Script Identification |
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2007 |
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9th International Conference on Document Analysis and Recognition |
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1 |
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551–555 |
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Curitiba (Brazil) |
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DAG @ dag @ CRP2007a |
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885 |
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Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal |
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Title |
SwinDocSegmenter: An End-to-End Unified Domain Adaptive Transformer for Document Instance Segmentation |
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2023 |
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17th International Conference on Document Analysis and Recognition |
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14187 |
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307–325 |
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Instance-level segmentation of documents consists in assigning a class-aware and instance-aware label to each pixel of the image. It is a key step in document parsing for their understanding. In this paper, we present a unified transformer encoder-decoder architecture for en-to-end instance segmentation of complex layouts in document images. The method adapts a contrastive training with a mixed query selection for anchor initialization in the decoder. Later on, it performs a dot product between the obtained query embeddings and the pixel embedding map (coming from the encoder) for semantic reasoning. Extensive experimentation on competitive benchmarks like PubLayNet, PRIMA, Historical Japanese (HJ), and TableBank demonstrate that our model with SwinL backbone achieves better segmentation performance than the existing state-of-the-art approaches with the average precision of 93.72, 54.39, 84.65 and 98.04 respectively under one billion parameters. The code is made publicly available at: github.com/ayanban011/SwinDocSegmenter . |
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San Jose; CA; USA; August 2023 |
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DAG |
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Admin @ si @ BBL2023 |
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3893 |
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Alicia Fornes; Sergio Escalera; Josep Llados; Ernest Valveny |
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Title |
Symbol Classification using Dynamic Aligned Shape Descriptor |
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2010 |
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20th International Conference on Pattern Recognition |
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1957–1960 |
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Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we propose a new descriptor and distance computation for coping with the problem of symbol recognition in the domain of Graphical Document Image Analysis. The proposed D-Shape descriptor encodes the arrangement information of object parts in a circular structure, allowing different levels of distortion. The classification is performed using a cyclic Dynamic Time Warping based method, allowing distortions and rotation. The methodology has been validated on different data sets, showing very high recognition rates. |
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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; HUPBA; MILAB |
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BCNPCL @ bcnpcl @ FEL2010 |
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1421 |
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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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DAS |
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Admin @ si @ NTR2008a |
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1873 |
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Oriol Ramos Terrades; Salvatore Tabbone; L. Wendling; Ernest Valveny |
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Symbol Recognition based on a Multiresolution Analysis of the Radon Transform |
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2004 |
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The International Workshop on Multidisciplinary Image, Video, and Audio Retrieval and Mining |
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Sherbrooke (Canada) |
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DAG |
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DAG @ dag @ RTW2004 |
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500 |
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Josep Llados; Enric Marti; Juan J.Villanueva |
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Symbol recognition by error-tolerant subgraph matching between region adjacency graphs |
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2001 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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23 |
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10 |
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1137-1143 |
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The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content. |
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DAG;IAM;ISE; |
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IAM @ iam @ LMV2001 |
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1581 |
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Alicia Fornes; Sergio Escalera; Josep Llados; Gemma Sanchez |
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Symbol Recognition by Multi-class Blurred Shape Models |
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2007 |
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Seventh IAPR International Workshop on Graphics Recognition |
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11–13 |
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Curitiba (Brazil) |
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GREC |
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DAG; MILAB; HUPBA |
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BCNPCL @ bcnpcl @ FEL2007b |
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910 |
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Ernest Valveny; Philippe Dosch |
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Symbol Recognition Contest: A Synthesis |
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2004 |
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Graphics Recognition: Recent Advances and Perspectives, J. Llados, Y.B. Kwon (Eds.), Lecture Notes in Computer Science, 3088:368–386, ISBN: 3–540–22478–5 |
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DAG @ dag @ VaD2004b |
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