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Volkmar Frinken; Andreas Fischer; Markus Baumgartner; Horst Bunke |
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Title |
Keyword spotting for self-training of BLSTM NN based handwriting recognition systems |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
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PR |
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47 |
Issue ![sorted by Issue field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
3 |
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1073-1082 |
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Document retrieval; Keyword spotting; Handwriting recognition; Neural networks; Semi-supervised learning |
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The automatic transcription of unconstrained continuous handwritten text requires well trained recognition systems. The semi-supervised paradigm introduces the concept of not only using labeled data but also unlabeled data in the learning process. Unlabeled data can be gathered at little or not cost. Hence it has the potential to reduce the need for labeling training data, a tedious and costly process. Given a weak initial recognizer trained on labeled data, self-training can be used to recognize unlabeled data and add words that were recognized with high confidence to the training set for re-training. This process is not trivial and requires great care as far as selecting the elements that are to be added to the training set is concerned. In this paper, we propose to use a bidirectional long short-term memory neural network handwritten recognition system for keyword spotting in order to select new elements. A set of experiments shows the high potential of self-training for bootstrapping handwriting recognition systems, both for modern and historical handwritings, and demonstrate the benefits of using keyword spotting over previously published self-training schemes. |
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DAG; 600.077; 602.101 |
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Admin @ si @ FFB2014 |
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2297 |
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Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Embedding of Graphs with Discrete Attributes Via Label Frequencies |
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Journal Article |
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Year |
2013 |
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International Journal of Pattern Recognition and Artificial Intelligence |
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IJPRAI |
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27 |
Issue ![sorted by Issue field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
3 |
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1360002-1360029 |
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Discrete attributed graphs; graph embedding; graph classification |
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Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different nature, and it is shown to be competitive to state-of-the-art classification algorithms for graphs, while being computationally much more efficient. |
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Admin @ si @ GVB2013 |
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2305 |
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Anjan Dutta; Josep Llados; Umapada Pal |
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Title |
A symbol spotting approach in graphical documents by hashing serialized graphs |
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Journal Article |
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2013 |
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Pattern Recognition |
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PR |
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46 |
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3 |
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752-768 |
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Symbol spotting; Graphics recognition; Graph matching; Graph serialization; Graph factorization; Graph paths; Hashing |
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In this paper we propose a symbol spotting technique in graphical documents. Graphs are used to represent the documents and a (sub)graph matching technique is used to detect the symbols in them. We propose a graph serialization to reduce the usual computational complexity of graph matching. Serialization of graphs is performed by computing acyclic graph paths between each pair of connected nodes. Graph paths are one-dimensional structures of graphs which are less expensive in terms of computation. At the same time they enable robust localization even in the presence of noise and distortion. Indexing in large graph databases involves a computational burden as well. We propose a graph factorization approach to tackle this problem. Factorization is intended to create a unified indexed structure over the database of graphical documents. Once graph paths are extracted, the entire database of graphical documents is indexed in hash tables by locality sensitive hashing (LSH) of shape descriptors of the paths. The hashing data structure aims to execute an approximate k-NN search in a sub-linear time. We have performed detailed experiments with various datasets of line drawings and compared our method with the state-of-the-art works. The results demonstrate the effectiveness and efficiency of our technique. |
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Elsevier |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203; 601.152 |
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Admin @ si @ DLP2012 |
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2127 |
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Alicia Fornes; Anjan Dutta; Albert Gordo; Josep Llados |
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Title |
CVC-MUSCIMA: A Ground-Truth of Handwritten Music Score Images for Writer Identification and Staff Removal |
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Journal Article |
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2012 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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15 |
Issue ![sorted by Issue field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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243-251 |
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Music scores; Handwritten documents; Writer identification; Staff removal; Performance evaluation; Graphics recognition; Ground truths |
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0,405JCR
The analysis of music scores has been an active research field in the last decades. However, there are no publicly available databases of handwritten music scores for the research community. In this paper we present the CVC-MUSCIMA database and ground-truth of handwritten music score images. The dataset consists of 1,000 music sheets written by 50 different musicians. It has been especially designed for writer identification and staff removal tasks. In addition to the description of the dataset, ground-truth, partitioning and evaluation metrics, we also provide some base-line results for easing the comparison between different approaches. |
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1433-2833 |
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Admin @ si @ FDG2012 |
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2129 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Text line extraction in graphical documents using background and foreground |
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Journal Article |
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Year |
2012 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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15 |
Issue ![sorted by Issue field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
3 |
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227-241 |
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0,405 JCR
In graphical documents (e.g., maps, engineering drawings), artistic documents etc., the text lines are annotated in multiple orientations or curvilinear way to illustrate different locations or symbols. For the optical character recognition of such documents, individual text lines from the documents need to be extracted. In this paper, we propose a novel method to segment such text lines and the method is based on the foreground and background information of the text components. To effectively utilize the background information, a water reservoir concept is used here. In the proposed scheme, at first, individual components are detected and grouped into character clusters in a hierarchical way using size and positional information. Next, the clusters are extended in two extreme sides to determine potential candidate regions. Finally, with the help of these candidate regions,
individual lines are extracted. The experimental results are presented on different datasets of graphical documents, camera-based warped documents, noisy images containing seals, etc. The results demonstrate that our approach is robust and invariant to size and orientation of the text lines present in
the document. |
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1433-2833 |
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DAG |
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Admin @ si @ RPL2012b |
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2134 |
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