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Author |
Jon Almazan; Alicia Fornes; Ernest Valveny |
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Title  |
A Non-Rigid Feature Extraction Method for Shape Recognition |
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Conference Article |
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Year |
2011 |
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11th International Conference on Document Analysis and Recognition |
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987-991 |
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This paper presents a methodology for shape recognition that focuses on dealing with the difficult problem of large deformations. The proposed methodology consists in a novel feature extraction technique, which uses a non-rigid representation adaptable to the shape. This technique employs a deformable grid based on the computation of geometrical centroids that follows a region partitioning algorithm. Then, a feature vector is extracted by computing pixel density measures around these geometrical centroids. The result is a shape descriptor that adapts its representation to the given shape and encodes the pixel density distribution. The validity of the method when dealing with large deformations has been experimentally shown over datasets composed of handwritten shapes. It has been applied to signature verification and shape recognition tasks demonstrating high accuracy and low computational cost. |
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Beijing; China; September 2011 |
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978-0-7695-4520-2 |
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ICDAR |
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DAG |
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Admin @ si @ AFV2011 |
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1763 |
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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 |
Palaiahnakote Shivakumara; Anjan Dutta; Trung Quy Phan; Chew Lim Tan; Umapada Pal |
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Title |
A Novel Mutual Nearest Neighbor based Symmetry for Text Frame Classification in Video |
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Journal Article |
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Year |
2011 |
Publication |
Pattern Recognition |
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PR |
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44 |
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8 |
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1671-1683 |
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In the field of multimedia retrieval in video, text frame classification is essential for text detection, event detection, event boundary detection, etc. We propose a new text frame classification method that introduces a combination of wavelet and median moment with k-means clustering to select probable text blocks among 16 equally sized blocks of a video frame. The same feature combination is used with a new Max–Min clustering at the pixel level to choose probable dominant text pixels in the selected probable text blocks. For the probable text pixels, a so-called mutual nearest neighbor based symmetry is explored with a four-quadrant formation centered at the centroid of the probable dominant text pixels to know whether a block is a true text block or not. If a frame produces at least one true text block then it is considered as a text frame otherwise it is a non-text frame. Experimental results on different text and non-text datasets including two public datasets and our own created data show that the proposed method gives promising results in terms of recall and precision at the block and frame levels. Further, we also show how existing text detection methods tend to misclassify non-text frames as text frames in term of recall and precision at both the block and frame levels. |
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Admin @ si @ SDP2011 |
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1727 |
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Author |
Jose Antonio Rodriguez; Gemma Sanchez; Josep Llados |
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Title |
A Pen-based Interface for Real-time Document Edition |
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Conference Article |
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2007 |
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9th International Conference on Document Analysis and Recognition. |
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2 |
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939–944 |
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Curitiba (Brazil) |
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ICDAR |
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DAG @ dag @ RSL2007b |
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883 |
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Author |
Mathieu Nicolas Delalandre; Jean-Yves Ramel; Ernest Valveny; Muhammad Muzzamil Luqman |
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Title |
A Performance Characterization Algorithm for Symbol Localization |
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Conference Article |
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Year |
2009 |
Publication |
8th IAPR International Workshop on Graphics Recognition |
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3-11 |
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In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm is aimed to be a more “reliable” and “open” solution to characterize the performance. To achieve that, it exploits only single points as the result of localization and offers the possibility to reconsider the localization results provided by a system. We use the information about context in groundtruth, and overall localization results, to detect the ambiguous localization results. A probability score is computed for each matching between a localization point and a groundtruth region, depending on the spatial distribution of the other regions in the groundtruth. Final characterization is given with detection rate/probability score plots, describing the sets of possible interpretations of the localization results, according to a given confidence rate. We present experimentation details along with the results for the symbol localization system of [1], exploiting a synthetic dataset of architectural floorplans and electrical diagrams (composed of 200 images and 3861 symbols). |
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La Rochelle; July 2009 |
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Springer |
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GREC |
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DAG |
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DAG @ dag @ DRV2009 |
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1443 |
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Author |
Mathieu Nicolas Delalandre; Jean-Yves Ramel; Ernest Valveny; Muhammad Muzzamil Luqman |
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Title |
A Performance Characterization Algorithm for Symbol Localization |
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Book Chapter |
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2010 |
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Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers |
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6020 |
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260–271 |
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Abstract |
In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm is aimed to be a more “reliable” and “open” solution to characterize the performance. To achieve that, it exploits only single points as the result of localization and offers the possibility to reconsider the localization results provided by a system. We use the information about context in groundtruth, and overall localization results, to detect the ambiguous localization results. A probability score is computed for each matching between a localization point and a groundtruth region, depending on the spatial distribution of the other regions in the groundtruth. Final characterization is given with detection rate/probability score plots, describing the sets of possible interpretations of the localization results, according to a given confidence rate. We present experimentation details along with the results for the symbol localization system of [1], exploiting a synthetic dataset of architectural floorplans and electrical diagrams (composed of 200 images and 3861 symbols). |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-13727-3 |
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GREC |
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DAG |
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Admin @ si @ DRV2010 |
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2406 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices |
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Journal Article |
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2009 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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12 |
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2 |
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83-96 |
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Performance evaluation; Symbol Spotting; Graphics Recognition |
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Symbol spotting systems are intended to retrieve regions of interest from a document image database where the queried symbol is likely to be found. They shall have the ability to recognize and locate graphical symbols in a single step. In this paper, we present a set of measures to evaluate the performance of a symbol spotting system in terms of recognition abilities, location accuracy and scalability. We show that the proposed measures allow to determine the weaknesses and strengths of different methods. In particular we have tested a symbol spotting method based on a set of four different off-the-shelf shape descriptors. |
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1433-2833 |
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DAG |
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DAG @ dag @ RuL2009a |
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1166 |
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Author |
Gemma Sanchez; Ernest Valveny; Josep Llados; Joan Mas; N. Lozano |
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Title |
A platform to extract knowledge from graphic documents. Application to an architectural sketch understanding scenario |
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Miscellaneous |
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2004 |
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Document Analysis Systems VI, S. Marinai, A. Dengel (Eds.) Lecture Notes in Computer Science, 3163:389–400 |
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Springer-Verlag |
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DAG @ dag @ SVL2004 |
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460 |
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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 |
Publication |
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 |
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DAG @ dag @ NKV2010 |
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1436 |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
A probabilistic framework for handwritten text line segmentation |
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Miscellaneous |
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2018 |
Publication |
Arxiv |
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Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning |
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We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer
vision tasks. In this paper, we apply it to handwritten text line segmentation.
We conduct several experiments that demonstrate that our method deal with
common issues of this task, such as complex document layout or non-latin
scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine
tuning step. |
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DAG; 600.097; 600.121 |
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Admin @ si @ CrR2018 |
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3253 |
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