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Author |
Suman Ghosh; Ernest Valveny |
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Title |
Query by String word spotting based on character bi-gram indexing |
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Conference Article |
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Year |
2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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881-885 |
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In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC). These attribute models are learned using linear SVMs over the Fisher Vector representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi- gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasets |
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Nancy; France; August 2015 |
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ICDAR |
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DAG; 600.077 |
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Call Number |
Admin @ si @ GhV2015a |
Serial  |
2715 |
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Author |
Marçal Rusiñol; V. Poulain d'Andecy; Dimosthenis Karatzas; Josep Llados |
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Title |
Classification of Administrative Document Images by Logo Identification |
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Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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8746 |
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49-58 |
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Administrative Document Classification; Logo Recognition; Logo Spotting |
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This paper is focused on the categorization of administrative document images (such as invoices) based on the recognition of the supplier’s graphical logo. Two different methods are proposed, the first one uses a bag-of-visual-words model whereas the second one tries to locate logo images described by the blurred shape model descriptor within documents by a sliding-window technique. Preliminar results are reported with a dataset of real administrative documents. |
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Springer Berlin Heidelberg |
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Bart Lamiroy; Jean-Marc Ogier |
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0302-9743 |
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978-3-662-44853-3 |
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Notes |
DAG; 600.056; 600.045; 605.203; 600.077 |
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Admin @ si @ RPK2014 |
Serial |
2701 |
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Author |
Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Spotting Graphical Symbols in Camera-Acquired Documents in Real Time |
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Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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8746 |
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3-10 |
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In this paper we present a system devoted to spot graphical symbols in camera-acquired document images. The system is based on the extraction and further matching of ORB compact local features computed over interest key-points. Then, the FLANN indexing framework based on approximate nearest neighbor search allows to efficiently match local descriptors between the captured scene and the graphical models. Finally, the RANSAC algorithm is used in order to compute the homography between the spotted symbol and its appearance in the document image. The proposed approach is efficient and is able to work in real time. |
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Springer Berlin Heidelberg |
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Bart Lamiroy; Jean-Marc Ogier |
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0302-9743 |
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978-3-662-44853-3 |
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DAG; 600.045; 600.055; 600.061; 600.077 |
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Admin @ si @ RKL2014 |
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2700 |
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Author |
Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados |
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Title |
Hierarchical Plausibility-Graphs for Symbol Spotting in Graphical Documents |
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Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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8746 |
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Pages |
25-37 |
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Abstract |
Graph representation of graphical documents often suffers from noise such as spurious nodes and edges, and their discontinuity. In general these errors occur during the low-level image processing viz. binarization, skeletonization, vectorization etc. Hierarchical graph representation is a nice and efficient way to solve this kind of problem by hierarchically merging node-node and node-edge depending on the distance. But the creation of hierarchical graph representing the graphical information often uses hard thresholds on the distance to create the hierarchical nodes (next state) of the lower nodes (or states) of a graph. As a result, the representation often loses useful information. This paper introduces plausibilities to the nodes of hierarchical graph as a function of distance and proposes a modified algorithm for matching subgraphs of the hierarchical graphs. The plausibility-annotated nodes help to improve the performance of the matching algorithm on two hierarchical structures. To show the potential of this approach, we conduct an experiment with the SESYD dataset. |
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Springer Berlin Heidelberg |
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Bart Lamiroy; Jean-Marc Ogier |
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0302-9743 |
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978-3-662-44853-3 |
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Notes |
DAG; 600.045; 600.056; 600.061; 600.077 |
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Call Number |
Admin @ si @ BDJ2014 |
Serial |
2699 |
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Author |
Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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Title |
A Product Graph Based Method for Dual Subgraph Matching Applied to Symbol Spotting |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
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8746 |
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Pages |
7-11 |
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Keywords |
Product graph; Dual edge graph; Subgraph matching; Random walks; Graph kernel |
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Abstract |
Product graph has been shown as a way for matching subgraphs. This paper reports the extension of the product graph methodology for subgraph matching applied to symbol spotting in graphical documents. Here we focus on the two major limitations of the previous version of the algorithm: (1) spurious nodes and edges in the graph representation and (2) inefficient node and edge attributes. To deal with noisy information of vectorized graphical documents, we consider a dual edge graph representation on the original graph representing the graphical information and the product graph is computed between the dual edge graphs of the pattern graph and the target graph. The dual edge graph with redundant edges is helpful for efficient and tolerating encoding of the structural information of the graphical documents. The adjacency matrix of the product graph locates the pair of similar edges of two operand graphs and exponentiating the adjacency matrix finds similar random walks of greater lengths. Nodes joining similar random walks between two graphs are found by combining different weighted exponentials of adjacency matrices. An experimental investigation reveals that the recall obtained by this approach is quite encouraging. |
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Springer Berlin Heidelberg |
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Bart Lamiroy; Jean-Marc Ogier |
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0302-9743 |
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978-3-662-44853-3 |
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Notes |
DAG; 600.077 |
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Call Number |
Admin @ si @ DLB2014 |
Serial |
2698 |
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Author |
Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez |
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Title |
Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies |
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Conference Article |
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2013 |
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10th IAPR International Workshop on Graphics Recognition |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG |
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no |
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Admin @ si @ HVS2013b |
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2696 |
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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 |
Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans |
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Conference Article |
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2013 |
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10th IAPR International Workshop on Graphics Recognition |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG; 600.045; 600.061; 600.056 |
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Admin @ si @ HFF2013b |
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2695 |
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Author |
J.Kuhn; A.Nussbaumer; J.Pirker; Dimosthenis Karatzas; A. Pagani; O.Conlan; M.Memmel; C.M.Steiner; C.Gutl; D.Albert; Andreas Dengel |
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Title |
Advancing Physics Learning Through Traversing a Multi-Modal Experimentation Space |
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Conference Article |
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2015 |
Publication |
Workshop Proceedings on the 11th International Conference on Intelligent Environments |
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19 |
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373-380 |
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Abstract |
Translating conceptual knowledge into real world experiences presents a significant educational challenge. This position paper presents an approach that supports learners in moving seamlessly between conceptual learning and their application in the real world by bringing physical and virtual experiments into everyday settings. Learners are empowered in conducting these situated experiments in a variety of physical settings by leveraging state of the art mobile, augmented reality, and virtual reality technology. A blend of mobile-based multi-sensory physical experiments, augmented reality and enabling virtual environments can allow learners to bridge their conceptual learning with tangible experiences in a completely novel manner. This approach focuses on the learner by applying self-regulated personalised learning techniques, underpinned by innovative pedagogical approaches and adaptation techniques, to ensure that the needs and preferences of each learner are catered for individually. |
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Praga; Chzech Republic; July 2015 |
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IE |
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DAG; 600.077 |
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Admin @ si @ KNP2015 |
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2694 |
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Author |
Suman Ghosh; Lluis Gomez; Dimosthenis Karatzas; Ernest Valveny |
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Title |
Efficient indexing for Query By String text retrieval |
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Conference Article |
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Year |
2015 |
Publication |
6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 |
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1236 - 1240 |
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This paper deals with Query By String word spotting in scene images. A hierarchical text segmentation algorithm based on text specific selective search is used to find text regions. These regions are indexed per character n-grams present in the text region. An attribute representation based on Pyramidal Histogram of Characters (PHOC) is used to compare text regions with the query text. For generation of the index a similar attribute space based Pyramidal Histogram of character n-grams is used. These attribute models are learned using linear SVMs over the Fisher Vector [1] representation of the images along with the PHOC labels of the corresponding strings. |
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Nancy; France; August 2015 |
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CBDAR |
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DAG; 600.077 |
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Call Number |
Admin @ si @ GGK2015 |
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2693 |
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Author |
A.Nicolaou; Andrew Bagdanov; Marcus Liwicki; Dimosthenis Karatzas |
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Title |
Sparse Radial Sampling LBP for Writer Identification |
Type |
Conference Article |
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Year |
2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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716-720 |
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In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features. |
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Nancy; France; August 2015 |
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ICDAR |
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DAG; 600.077 |
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Call Number |
Admin @ si @ NBL2015 |
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2692 |
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