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Author |
Joan Mas; Josep Llados; Gemma Sanchez; J.A. Jorge |


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Title |
A syntactic approach based on distortion-tolerant Adjacency Grammars and a spatial-directed parser to interpret sketched diagrams |
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Journal Article |
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Year |
2010 |
Publication |
Pattern Recognition |
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PR |
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43 |
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12 |
Pages |
4148–4164 |
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Syntactic Pattern Recognition; Symbol recognition; Diagram understanding; Sketched diagrams; Adjacency Grammars; Incremental parsing; Spatial directed parsing |
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Abstract |
This paper presents a syntactic approach based on Adjacency Grammars (AG) for sketch diagram modeling and understanding. Diagrams are a combination of graphical symbols arranged according to a set of spatial rules defined by a visual language. AG describe visual shapes by productions defined in terms of terminal and non-terminal symbols (graphical primitives and subshapes), and a set functions describing the spatial arrangements between symbols. Our approach to sketch diagram understanding provides three main contributions. First, since AG are linear grammars, there is a need to define shapes and relations inherently bidimensional using a sequential formalism. Second, our parsing approach uses an indexing structure based on a spatial tessellation. This serves to reduce the search space when finding candidates to produce a valid reduction. This allows order-free parsing of 2D visual sentences while keeping combinatorial explosion in check. Third, working with sketches requires a distortion model to cope with the natural variations of hand drawn strokes. To this end we extended the basic grammar with a distortion measure modeled on the allowable variation on spatial constraints associated with grammar productions. Finally, the paper reports on an experimental framework an interactive system for sketch analysis. User tests performed on two real scenarios show that our approach is usable in interactive settings. |
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DAG @ dag @ MLS2010 |
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1336 |
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Author |
Umapada Pal; Partha Pratim Roy; N. Tripathya; Josep Llados |


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Title |
Multi-oriented Bangla and Devnagari text recognition |
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Journal Article |
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Year |
2010 |
Publication |
Pattern Recognition |
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PR |
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43 |
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12 |
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4124–4136 |
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There are printed complex documents where text lines of a single page may have different orientations or the text lines may be curved in shape. As a result, it is difficult to detect the skew of such documents and hence character segmentation and recognition of such documents are a complex task. In this paper, using background and foreground information we propose a novel scheme towards the recognition of Indian complex documents of Bangla and Devnagari script. In Bangla and Devnagari documents usually characters in a word touch and they form cavity regions. To take care of these cavity regions, background information of such documents is used. Convex hull and water reservoir principle have been applied for this purpose. Here, at first, the characters are segmented from the documents using the background information of the text. Next, individual characters are recognized using rotation invariant features obtained from the foreground part of the characters.
For character segmentation, at first, writing mode of a touching component (word) is detected using water reservoir principle based features. Next, depending on writing mode and the reservoir base-region of the touching component, a set of candidate envelope points is then selected from the contour points of the component. Based on these candidate points, the touching component is finally segmented into individual characters. For recognition of multi-sized/multi-oriented characters the features are computed from different angular information obtained from the external and internal contour pixels of the characters. These angular information are computed in such a way that they do not depend on the size and rotation of the characters. Circular and convex hull rings have been used to divide a character into smaller zones to get zone-wise features for higher recognition results. We combine circular and convex hull features to improve the results and these features are fed to support vector machines (SVM) for recognition. From our experiment we obtained recognition results of 99.18% (98.86%) accuracy when tested on 7515 (7874) Devnagari (Bangla) characters. |
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DAG @ dag @ PRT2010 |
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1337 |
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Author |
Anjan Dutta |

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Title |
Symbol Spotting in Graphical Documents by Serialized Subgraph Matching |
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Report |
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2010 |
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CVC Technical Report |
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159 |
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Master's thesis |
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Admin @ si @ Dut2010 |
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1351 |
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Author |
David Fernandez |

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Title |
Handwritten Word Spotting in Old Manuscript Images using Shape Descriptors |
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Report |
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2010 |
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CVC Technical Report |
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161 |
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Master's thesis |
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Admin @ si @ Fer2010b |
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1353 |
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Author |
Debora Gil; Jordi Gonzalez; Gemma Sanchez (eds) |

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Computer Vision: Advances in Research and Development |
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2007 |
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Proceedings of the 2nd CVC International Workshop |
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UAB |
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Bellaterra (Spain) |
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Debora Gil; Jordi Gonzalez; Gemma Sanchez |
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978-84-935251-4-9 |
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IAM; ISE; DAG |
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IAM @ iam @ GGS2007 |
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1493 |
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Author |
Gemma Sanchez; Josep Llados; Enric Marti |


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Title |
A string-based method to recognize symbols and structural textures in architectural plans |
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Conference Article |
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Year |
1997 |
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2nd IAPR Workshop on Graphics Recognition |
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91-103 |
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This paper deals with the recognition of symbols and struc- tural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clus- tering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the simila- rity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion. |
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Nancy, France |
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DAG; IAM |
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IAM @ iam @ SLE1997 |
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1498 |
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Author |
Jaime Lopez-Krahe; Josep Llados; Enric Marti |


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Title |
Architectural Floor Plan Analysis |
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2000 |
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CVonline |
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Edimburg, UK |
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University of Edinburgh |
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Robert B. Fisher |
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online pdf |
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DAG;IAM |
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IAM @ iam @ LLM2000 |
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1561 |
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Author |
Josep Llados; Horst Bunke; Enric Marti |


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Title |
Finding rotational symmetries by cyclic string matching |
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Journal Article |
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Year |
1997 |
Publication |
Pattern recognition letters |
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PRL |
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18 |
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14 |
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1435-1442 |
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Rotational symmetry; Reflectional symmetry; String matching |
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Symmetry is an important shape feature. In this paper, a simple and fast method to detect perfect and distorted rotational symmetries of 2D objects is described. The boundary of a shape is polygonally approximated and represented as a string. Rotational symmetries are found by cyclic string matching between two identical copies of the shape string. The set of minimum cost edit sequences that transform the shape string to a cyclically shifted version of itself define the rotational symmetry and its order. Finally, a modification of the algorithm is proposed to detect reflectional symmetries. Some experimental results are presented to show the reliability of the proposed algorithm |
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Elsevier |
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DAG;IAM; |
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IAM @ iam @ LBM1997a |
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1562 |
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Author |
Josep Llados;Horst Bunke; Enric Marti |


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Title |
Using Cyclic String Matching to Find Rotational and Reflectional Symmetries in Shapes |
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1997 |
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Intelligent Robots: Sensing, Modeling and Planning |
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164-179 |
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Dagstuhl Workshop |
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World Scientific Press |
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9810231857 |
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DAG;IAM; |
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IAM @ iam @ LBM1997b |
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1563 |
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Author |
Josep Llados; Horst Bunke; Enric Marti |

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Using cyclic string matching to find rotational and reflectional symmetric shapes |
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Conference Article |
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1996 |
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Intelligent Robots: Sensing, Modeling and Planning (Dagstuhl Workshop) |
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164-179 |
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World Scientific |
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Saarbrucken (Germany). |
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R.C. Bolles, H.B.H.N. |
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DAG;IAM |
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IAM @ iam @ LBM1996 |
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1564 |
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